In [1]:
import numpy as np 
import pickle
import pandas as pd
import glob
import matplotlib.pyplot as plt
from tqdm._tqdm_notebook import tqdm_notebook as tqdm

plt.style.use('seaborn')
import matplotlib.colors as mcolors
cols = mcolors.TABLEAU_COLORS
cols = list(cols.keys())
cols = cols+cols
In [260]:
glob.glob("sims6/*")
Out[260]:
['sims6/archive0528',
 'sims6/archive0608',
 'sims6/s1',
 'sims6/archive0527',
 'sims6/archive0415',
 'sims6/archive0408',
 'sims6/archive06082',
 'sims6/s3',
 'sims6/archiveM3',
 'sims6/archive0412',
 'sims6/s2',
 'sims6/archive0414',
 'sims6/archive0505',
 'sims6/archive0525',
 'sims6/archive0312',
 'sims6/archive0524',
 'sims6/archiveM2',
 'sims6/archive0603',
 'sims6/archive0416',
 'sims6/archive0530',
 'sims6/s4',
 'sims6/archive0427',
 'sims6/archive0413',
 'sims6/archives40']
In [ ]:
 
In [12]:
np.array(a)
np.array(b)
Out[12]:
array([[ 1.        ,  0.        ],
       [ 1.19055524,  1.        ],
       [ 0.51264446,  0.95274419],
       [ 0.4737958 ,  0.7702899 ],
       [ 0.74614907, -0.16922176],
       [ 0.57871728,  0.03200706],
       [ 1.44796937, -0.23529181]])
In [13]:
dat = np.load(FOLDERS[0] + '/SSDF_Simu.npz')
In [14]:
ghg.mean(0)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-14-a77913ecff0d> in <module>
----> 1 ghg.mean(0)

NameError: name 'ghg' is not defined
In [ ]:
incides = [0,1, 3,5,7]
In [2226]:
FOLDERS = sorted(glob.glob('sims6/s*'))
#FOLDERS = [FOLDERS[i] for i in incides]
print(FOLDERS)
def ab(folder):
    #dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = np.median(ghg,0)
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = np.median(bhg,0).reshape(-1,2)[:-1,:] #bhg.mean(0).reshape(-1,2)[:-1,:]
    #print(b)
    
    a2 = np.median(ghg,0)#.median(0)
    
    b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    return a,b, a2, b2


ea, eb, ea2, eb2 = ab(FOLDERS[0])
a = pd.read_csv('sims6/a.csv',index_col=0)
b = pd.read_csv('sims6/b.csv',index_col=0)
delta = [1,-1]
L = a.shape[0]
EP = len(FOLDERS)
ap = np.zeros((EP, L))
bp1 = np.zeros((EP, L-1))
bp2 = np.zeros((EP, L-1))


ap5 = np.zeros((EP, L))
bp15 = np.zeros((EP, L-1))
bp25 = np.zeros((EP, L-1))
for i in tqdm(range(EP)):
    try:
        ea, eb, ea2, eb2 = ab(FOLDERS[i])
        ap[i] = ea
        bp1[i] = eb[:,0]
        bp2[i] = eb[:,1]
        
        ap5[i] = ea2
        bp15[i] = eb2[:,0]
        bp25[i] = eb2[:,1]
        
    except:
        pass
    
['sims6/s1', 'sims6/s10', 'sims6/s11', 'sims6/s12', 'sims6/s13', 'sims6/s14', 'sims6/s15', 'sims6/s16', 'sims6/s17', 'sims6/s18', 'sims6/s19', 'sims6/s2', 'sims6/s20', 'sims6/s21', 'sims6/s22', 'sims6/s23', 'sims6/s24', 'sims6/s25', 'sims6/s26', 'sims6/s27', 'sims6/s28', 'sims6/s29', 'sims6/s3', 'sims6/s30', 'sims6/s31', 'sims6/s32', 'sims6/s33', 'sims6/s34', 'sims6/s35', 'sims6/s36', 'sims6/s37', 'sims6/s38', 'sims6/s39', 'sims6/s4', 'sims6/s40', 'sims6/s5', 'sims6/s6', 'sims6/s7', 'sims6/s8', 'sims6/s9']

In [2227]:
#plt.plot(ap)
plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
for i in range(L):
    plt.plot(ap[:,i],color=cols[i])
    plt.axhline(np.array(a)[i],ls='--', color= cols[i] )
plt.title('A_LOADING', fontsize=15)
#plt.show()


plt.subplot(2,2,3)
#plt.plot(bp1)
for i in range(L-1):
    plt.plot(bp1[:,i],color=cols[i])
    plt.axhline(np.array(np.array(b)[i,0]),ls='--', color= cols[i])
plt.title('B_LOADING_1', fontsize=15)
#plt.show()

plt.subplot(2,2,4)
#plt.plot(bp2)
for i in range(L-1):
    plt.plot(bp2[:,i],color=cols[i])
    plt.axhline(np.array(np.array(b)[i,1]),ls='--', color=cols[i])
plt.title('B_LOADING_2', fontsize=15)    
#plt.savefig('40sims.png')
plt.show()
In [4]:
FOLDERS = sorted(glob.glob('sims6/archives40/s*'))
#FOLDERS = [FOLDERS[i] for i in incides]
print(FOLDERS)
def ab(folder):
    #dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = ghg[-20000:] #np.median(ghg,0)
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = bhg[-20000:]#np.median(bhg,0).reshape(-1,2)[:-1,:] #bhg.mean(0).reshape(-1,2)[:-1,:]
    #print(b)
    
    a2 = np.median(ghg,0)#.median(0)
    
    b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    return a,b, a2, b2


ea, eb, ea2, eb2 = ab(FOLDERS[0])
a = pd.read_csv('sims6/archives40/a.csv',index_col=0)
b = pd.read_csv('sims6/archives40/b.csv',index_col=0)
delta = [1,-1]
L = a.shape[0]
EP = len(FOLDERS)
ap = np.zeros((EP, 20000, L))
bp1 = np.zeros((EP, 20000, L-1))
bp2 = np.zeros((EP, 20000, L-1))


ap5 = np.zeros((EP, L))
bp15 = np.zeros((EP, L-1))
bp25 = np.zeros((EP, L-1))
for i in tqdm(range(EP)):
    try:
        ea, eb, ea2, eb2 = ab(FOLDERS[i])
        ap[i] = ea
        bp1[i] = eb[:,[i*2 for i in range(L-1)]]
        bp2[i] = eb[:,[i*2+1 for i in range(L-1)]]
        
        ap5[i] = ea2
        bp15[i] = eb2[:,0]
        bp25[i] = eb2[:,1]
        
    except:
        pass
    
['sims6/archives40/s1', 'sims6/archives40/s10', 'sims6/archives40/s11', 'sims6/archives40/s12', 'sims6/archives40/s13', 'sims6/archives40/s14', 'sims6/archives40/s15', 'sims6/archives40/s16', 'sims6/archives40/s17', 'sims6/archives40/s18', 'sims6/archives40/s19', 'sims6/archives40/s2', 'sims6/archives40/s20', 'sims6/archives40/s21', 'sims6/archives40/s22', 'sims6/archives40/s23', 'sims6/archives40/s24', 'sims6/archives40/s25', 'sims6/archives40/s26', 'sims6/archives40/s27', 'sims6/archives40/s28', 'sims6/archives40/s29', 'sims6/archives40/s3', 'sims6/archives40/s30', 'sims6/archives40/s31', 'sims6/archives40/s32', 'sims6/archives40/s33', 'sims6/archives40/s34', 'sims6/archives40/s35', 'sims6/archives40/s36', 'sims6/archives40/s37', 'sims6/archives40/s38', 'sims6/archives40/s39', 'sims6/archives40/s4', 'sims6/archives40/s40', 'sims6/archives40/s5', 'sims6/archives40/s6', 'sims6/archives40/s7', 'sims6/archives40/s8', 'sims6/archives40/s9']

In [27]:
plt.boxplot( ap.mean(1) )
plt.plot(a,ls='',marker="o",markersize=10, color="red")
plt.show()
In [ ]:
 
In [29]:
b
Out[29]:
V1 V2
1 1.000000 0.000000
2 -0.194057 1.000000
3 -0.006770 -0.367838
4 -0.075788 -0.159197
5 0.706906 0.368849
6 0.854405 0.278909
7 -0.347586 0.335468
8 -0.149342 0.827188
9 0.952103 0.590222
10 -0.608907 -0.599121
11 -0.670391 -0.961221
12 0.989352 -0.303130
In [40]:
#plt.plot(ap)
plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
plt.boxplot(ap.mean(1))
plt.plot(a,ls='',marker="o",markersize=10, color="red")
#for i in range(L):
#    plt.boxplot(ap[:,i],color=cols[i])
#    plt.axhline(np.array(a)[i],ls='--', color= cols[i] )
plt.title('A_LOADING', fontsize=15)
#plt.show()


plt.subplot(2,2,3)
#plt.plot(bp1)
plt.boxplot(bp1.mean(1))
plt.plot(b.iloc[:-1,0],ls='',marker="o",markersize=10, color="red")
plt.title('B_LOADING_1', fontsize=15)
#plt.show()

plt.subplot(2,2,4)
#plt.plot(bp2)
plt.boxplot(bp2.mean(1))
plt.plot(b.iloc[:-1,1],ls='',marker="o",markersize=10, color="red")
plt.title('B_LOADING_2', fontsize=15)    
plt.savefig('40sims_Box.png')
plt.show()
In [32]:
bp1.mean(1)
Out[32]:
array([[ 1.        , -0.29250535, -0.13070049, -0.20087518,  0.68111174,
         0.84427725, -0.41428571, -0.19798213,  0.87622289, -0.51171844,
        -0.71466083],
       [ 1.        , -0.25182113, -0.09777757, -0.21436633,  0.68778603,
         0.84463672, -0.43695172, -0.23783217,  0.88071709, -0.57876132,
        -0.74320185],
       [ 1.        , -0.32148242, -0.12393699, -0.23056507,  0.66065636,
         0.76265557, -0.43384951, -0.17296159,  0.93811533, -0.53716723,
        -0.6679827 ],
       [ 1.        , -0.30689591, -0.11192216, -0.18719816,  0.68348053,
         0.80620231, -0.40786846, -0.17899611,  0.95100826, -0.49403177,
        -0.70411727],
       [ 1.        , -0.2781988 , -0.11512807, -0.19095671,  0.6850927 ,
         0.80964548, -0.44523478, -0.25129914,  0.87574064, -0.59174782,
        -0.73700079],
       [ 1.        , -0.30443393, -0.12805636, -0.18520355,  0.71020292,
         0.84232029, -0.43182471, -0.21616591,  0.89932253, -0.56072794,
        -0.74103981],
       [ 1.        , -0.33307109, -0.15513294, -0.21108627,  0.62477114,
         0.81131935, -0.49472355, -0.2027863 ,  0.92432017, -0.52387464,
        -0.69342716],
       [ 1.        , -0.35422766, -0.1357207 , -0.21210381,  0.66275929,
         0.80661134, -0.47138906, -0.23762705,  0.86662276, -0.5632668 ,
        -0.73834041],
       [ 1.        , -0.3181454 , -0.1148936 , -0.25881533,  0.64158538,
         0.79138456, -0.43162974, -0.17363191,  0.91083566, -0.49870387,
        -0.68229938],
       [ 1.        , -0.27651761, -0.09068098, -0.19931748,  0.65128858,
         0.79421679, -0.45388401, -0.21404919,  0.87372379, -0.52832379,
        -0.70739592],
       [ 1.        , -0.32368373, -0.14022167, -0.20860951,  0.65106574,
         0.78111992, -0.46961361, -0.18722618,  0.92943162, -0.5439651 ,
        -0.71958659],
       [ 1.        , -0.23894932, -0.08165948, -0.14257546,  0.73941479,
         0.86360238, -0.38298371, -0.20333597,  0.89040656, -0.57111204,
        -0.72981161],
       [ 1.        , -0.28602468, -0.09085798, -0.15431656,  0.65023174,
         0.82736184, -0.39001212, -0.17438518,  0.90269509, -0.53793611,
        -0.69364718],
       [ 1.        , -0.32559108, -0.12152944, -0.1821365 ,  0.69502238,
         0.81524445, -0.42495264, -0.20901557,  0.87698481, -0.56053057,
        -0.69795539],
       [ 1.        , -0.32101631, -0.09358133, -0.19633434,  0.65271622,
         0.79839109, -0.43623736, -0.22926035,  0.88513627, -0.56079097,
        -0.69931437],
       [ 1.        , -0.30624718, -0.08888376, -0.19057246,  0.66868971,
         0.82243985, -0.42874339, -0.23286451,  0.88066531, -0.59664457,
        -0.75205223],
       [ 1.        , -0.24622558, -0.09945483, -0.19820736,  0.70960451,
         0.85227859, -0.41147823, -0.22890877,  0.88107776, -0.59411331,
        -0.73314792],
       [ 1.        , -0.29891884, -0.07966939, -0.19123665,  0.66563474,
         0.82711429, -0.42476432, -0.25882409,  0.88627642, -0.56368027,
        -0.71649192],
       [ 1.        , -0.25226615, -0.10911538, -0.18627109,  0.67028035,
         0.79449815, -0.42193043, -0.15693299,  0.90955677, -0.56538685,
        -0.72523596],
       [ 1.        , -0.28201101, -0.08666707, -0.17192009,  0.65988614,
         0.80387779, -0.42790356, -0.20182666,  0.86579456, -0.55342282,
        -0.72276037],
       [ 1.        , -0.26913284, -0.06570302, -0.18297847,  0.64905326,
         0.83842308, -0.43647669, -0.20448153,  0.87753152, -0.60190991,
        -0.74051139],
       [ 1.        , -0.33580701, -0.11187865, -0.18538144,  0.64154381,
         0.78792646, -0.43579758, -0.17633952,  0.89969564, -0.53109038,
        -0.70903921],
       [ 1.        , -0.26868807, -0.09423375, -0.18939549,  0.67096131,
         0.84028534, -0.42756482, -0.21147586,  0.83616199, -0.63385997,
        -0.79222523],
       [ 1.        , -0.26585781, -0.0927361 , -0.15716882,  0.69782845,
         0.84504718, -0.38972592, -0.17876079,  0.89723838, -0.55456785,
        -0.7223934 ],
       [ 1.        , -0.27447428, -0.1160945 , -0.18066395,  0.69016234,
         0.85182602, -0.40221147, -0.18874587,  0.89748909, -0.52466597,
        -0.7458878 ],
       [ 1.        , -0.30417359, -0.09927968, -0.19699203,  0.68338072,
         0.81257084, -0.39731853, -0.23193758,  0.92126907, -0.51102638,
        -0.69815072],
       [ 1.        , -0.24511271, -0.08863936, -0.15431034,  0.71758523,
         0.81270416, -0.42215184, -0.21306674,  0.85101671, -0.57037554,
        -0.72367968],
       [ 1.        , -0.34416616, -0.0794275 , -0.22546814,  0.69136645,
         0.81908064, -0.46468734, -0.24485094,  0.89652218, -0.56160281,
        -0.72927767],
       [ 1.        , -0.34176356, -0.09929448, -0.21736171,  0.66983388,
         0.81614909, -0.48573208, -0.2610697 ,  0.89534064, -0.57911642,
        -0.70650021],
       [ 1.        , -0.30395578, -0.14281961, -0.2066276 ,  0.65446835,
         0.78493071, -0.42447381, -0.23391756,  0.86543792, -0.58050558,
        -0.71630375],
       [ 1.        , -0.2701312 , -0.11906016, -0.21965531,  0.74322243,
         0.83052841, -0.44542474, -0.16324608,  0.94761098, -0.580553  ,
        -0.74077832],
       [ 1.        , -0.1954449 , -0.04853086, -0.16345936,  0.68788905,
         0.8553399 , -0.40870494, -0.24394539,  0.86128322, -0.60438172,
        -0.73937999],
       [ 1.        , -0.26881444, -0.12034425, -0.17159295,  0.65499282,
         0.78150139, -0.435144  , -0.21804713,  0.85345934, -0.60280697,
        -0.76505896],
       [ 1.        , -0.34594254, -0.14143138, -0.22487108,  0.70405991,
         0.85183877, -0.46189165, -0.27863736,  0.88224726, -0.59550625,
        -0.74193607],
       [ 1.        , -0.35518675, -0.13393508, -0.21058134,  0.67064263,
         0.78862155, -0.45923204, -0.30858882,  0.85287624, -0.54865911,
        -0.69016347],
       [ 1.        , -0.27212544, -0.13836211, -0.21260667,  0.70381076,
         0.84706629, -0.46813152, -0.21930796,  0.89139645, -0.59502079,
        -0.72222247],
       [ 1.        , -0.28812935, -0.12898003, -0.19689437,  0.64893861,
         0.81648175, -0.44630224, -0.17858641,  0.87087154, -0.53946601,
        -0.69789177],
       [ 1.        , -0.29244743, -0.12785677, -0.2132341 ,  0.67237719,
         0.8519811 , -0.43893772, -0.22116432,  0.90628622, -0.56759848,
        -0.76931313],
       [ 1.        , -0.25791167, -0.11588601, -0.18024606,  0.70240271,
         0.80500735, -0.43155092, -0.19180113,  0.87167565, -0.53906184,
        -0.71443095],
       [ 1.        , -0.32497594, -0.07342697, -0.16100135,  0.66735582,
         0.84280845, -0.40914881, -0.23101721,  0.90013366, -0.55132177,
        -0.68252901]])
In [37]:
np.array(b)[:-1,1].shape
Out[37]:
(11,)
In [2163]:
#dat = np.load('sims6/s1' + '/SSDF_Simu.npz')

def ab2(folder):
    dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = ghg
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = bhg
    deltag = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/phig.csv', header=None))
    
    mugamg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/gammu.csv', header=None))
    mupsig = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/psimu.csv', header=None))
    #print(b)
    
    #a2 = np.median(ghg,0)#.median(0)
     
    #b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    return a,b, deltag, mugamg, mupsig#, a2, b2

test = ab2('sims6/s2')
a = pd.read_csv('sims6/a.csv',index_col=0)
b = pd.read_csv('sims6/b.csv',index_col=0)
mugam = pd.read_csv('sims6/mugam.csv',index_col=0)
mupsi = pd.read_csv('sims6/mupsi.csv',index_col=0)

L = a.shape[0]
In [61]:
F = 'sims6/s1/SSDF_Simu/MCMC/'

psim = np.array(pd.read_csv(F+'psimu.csv', header=None))
gamm=  np.array(pd.read_csv(F+'gammu.csv', header=None))
In [62]:
psim.mean(0)
Out[62]:
array([ 0.04687663, -1.29434596, -0.42665001, -0.82571116,  1.12564406,
        1.09546225,  0.22937559,  0.5813954 , -0.08455649,  1.19991027,
       -0.30021056, -0.26001931])
In [2166]:
#plt.plot(ap)
plt.figure(figsize=(15,15))
plt.subplot(2,2,1)
for i in range(L):
    plt.plot(test[0][:,i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(a)[i],ls='--', color= cols[i])
plt.title('A_LOADING', fontsize=15)
#plt.show()

plt.subplot(2,2,2)
for i in range(2):
    plt.plot(test[2][:,i],color=cols[i], alpha=0.5)
    plt.axhline(delta[i],ls='--', color= cols[i] )
plt.title('Delta', fontsize=15)
#plt.show()

plt.subplot(2,2,3)
#plt.plot(bp1)
for i in range(L-1):
    plt.plot(test[1][:,i*2],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,0]),ls='--', color= cols[i])
plt.title('B_LOADING_1', fontsize=15)
#plt.show()

plt.subplot(2,2,4)
#plt.plot(bp2)
for i in range(L-1):
    plt.plot(test[1][:, i*2+1],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,1]),ls='--', color=cols[i])
plt.title('B_LOADING_2', fontsize=15)    



plt.savefig('Simu_E10.png')
plt.show()
In [130]:
#plt.plot(ap)
plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
for i in range(L):
    plt.plot(test[0][:,i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(a)[i],ls='--', color= cols[i])
plt.title('A_LOADING', fontsize=15)
#plt.show()

plt.subplot(2,2,2)
for i in range(2):
    plt.plot(test[2][:,i],color=cols[i], alpha=0.5)
    plt.axhline(delta[i],ls='--', color= cols[i] )
plt.title('Delta', fontsize=15)
#plt.show()

plt.subplot(2,2,3)
#plt.plot(bp1)
for i in range(L-1):
    plt.plot(test[1][:,i*2],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,0]),ls='--', color= cols[i])
plt.title('B_LOADING_1', fontsize=15)
#plt.show()

plt.subplot(2,2,4)
#plt.plot(bp2)
for i in range(L-1):
    plt.plot(test[1][:, i*2+1],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,1]),ls='--', color=cols[i])
plt.title('B_LOADING_2', fontsize=15)    

#plt.savefig('Simu_E100.png')
plt.show()
In [1417]:
test = ab2('sims6/s3')
#plt.plot(ap)
plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
for i in range(L):
    plt.plot(test[0][:,i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(a)[i],ls='--', color= cols[i])
plt.title('A_LOADING', fontsize=15)
#plt.show()

plt.subplot(2,2,2)
for i in range(2):
    plt.plot(test[2][:,i],color=cols[i], alpha=0.5)
    plt.axhline(delta[i],ls='--', color= cols[i] )
plt.title('Delta', fontsize=15)
#plt.show()

plt.subplot(2,2,3)
#plt.plot(bp1)
for i in range(L-1):
    plt.plot(test[1][:,i*2],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,0]),ls='--', color= cols[i])
plt.title('B_LOADING_1', fontsize=15)
#plt.show()

plt.subplot(2,2,4)
#plt.plot(bp2)
for i in range(L-1):
    plt.plot(test[1][:, i*2+1],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,1]),ls='--', color=cols[i])
plt.title('B_LOADING_2', fontsize=15)    
"""
plt.subplot(3,2,5)
for i in range(L):
    plt.plot(test[3][:, i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(mugam)[i],ls='--', color=cols[i])
plt.title('a_h', fontsize=15)    

plt.subplot(3,2,6)
for i in range(L-1):
    plt.plot(test[4][:, i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(mupsi)[i],ls='--', color=cols[i])
plt.title('b_h', fontsize=15)    
"""
#plt.savefig('Simu_E1000.png')
plt.show()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1417-42945e46d7b4> in <module>
----> 1 test = ab2('sims6/s3')
      2 #plt.plot(ap)
      3 plt.figure(figsize=(15,10))
      4 plt.subplot(2,2,1)
      5 for i in range(L):

NameError: name 'ab2' is not defined
In [478]:
#plt.plot(ap)
test = ab2('sims6/s2')
plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
for i in range(L):
    plt.plot(test[0][:,i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(a)[i],ls='--', color= cols[i])
plt.title('A_LOADING', fontsize=15)
#plt.show()

plt.subplot(2,2,2)
for i in range(2):
    plt.plot(test[2][:,i],color=cols[i], alpha=0.5)
    plt.axhline(delta[i],ls='--', color= cols[i] )
plt.title('Delta', fontsize=15)
#plt.show()

plt.subplot(2,2,3)
#plt.plot(bp1)
for i in range(L-1):
    plt.plot(test[1][:,i*2],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,0]),ls='--', color= cols[i])
plt.title('B_LOADING_1', fontsize=15)
#plt.show()

plt.subplot(2,2,4)
#plt.plot(bp2)
for i in range(L-1):
    plt.plot(test[1][:, i*2+1],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,1]),ls='--', color=cols[i])
plt.title('B_LOADING_2', fontsize=15)    
"""
plt.subplot(3,2,5)
for i in range(L):
    plt.plot(test[3][:, i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(mugam)[i],ls='--', color=cols[i])
plt.title('a_h', fontsize=15)    

plt.subplot(3,2,6)
for i in range(L-1):
    plt.plot(test[4][:, i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(mupsi)[i],ls='--', color=cols[i])
plt.title('b_h', fontsize=15)    
"""
plt.savefig('Simu_E100.png')
plt.show()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-478-8b9503c96e87> in <module>
      1 #plt.plot(ap)
----> 2 test = ab2('sims6/s2')
      3 plt.figure(figsize=(15,10))
      4 plt.subplot(2,2,1)
      5 for i in range(L):

NameError: name 'ab2' is not defined
In [1416]:
#plt.plot(ap)
test = ab2('sims6/s3')
plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
for i in range(L):
    plt.plot(test[0][:,i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(a)[i],ls='--', color= cols[i])
plt.title('A_LOADING', fontsize=15)
#plt.show()

plt.subplot(2,2,2)
for i in range(2):
    plt.plot(test[2][:,i],color=cols[i], alpha=0.5)
    plt.axhline(delta[i],ls='--', color= cols[i] )
plt.title('Delta', fontsize=15)
#plt.show()

plt.subplot(2,2,3)
#plt.plot(bp1)
for i in range(L-1):
    plt.plot(test[1][:,i*2],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,0]),ls='--', color= cols[i])
plt.title('B_LOADING_1', fontsize=15)
#plt.show()

plt.subplot(2,2,4)
#plt.plot(bp2)
for i in range(L-1):
    plt.plot(test[1][:, i*2+1],color=cols[i], alpha=0.5)
    plt.axhline(np.array(np.array(b)[i,1]),ls='--', color=cols[i])
plt.title('B_LOADING_2', fontsize=15)    
"""
plt.subplot(3,2,5)
for i in range(L):
    plt.plot(test[3][:, i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(mugam)[i],ls='--', color=cols[i])
plt.title('a_h', fontsize=15)    

plt.subplot(3,2,6)
for i in range(L-1):
    plt.plot(test[4][:, i],color=cols[i], alpha=0.5)
    plt.axhline(np.array(mupsi)[i],ls='--', color=cols[i])
plt.title('b_h', fontsize=15)    
"""
plt.savefig('Simu_E10.png')
plt.show()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1416-e4edaab571b3> in <module>
      1 #plt.plot(ap)
----> 2 test = ab2('sims6/s3')
      3 plt.figure(figsize=(15,10))
      4 plt.subplot(2,2,1)
      5 for i in range(L):

NameError: name 'ab2' is not defined
In [1255]:
#dat = np.load('sims6/s1' + '/SSDF_Simu.npz')

def ab3(folder):
    #dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/Static_Simu/MCMC/ghg.csv', header=None))
    a = ghg
    bhg = np.array(pd.read_csv(folder +'/Static_Simu/MCMC/bhg.csv', header=None))
    b = bhg
    #deltag = np.array(pd.read_csv(folder +'/Static_Simu/MCMC/phig.csv', header=None))
    L = ghg.shape[1]
    print(L)
    print(a.shape)
    
    mugamg = np.array(pd.read_csv(folder +'/Static_Simu/MCMC/gammu.csv', header=None))
    mupsig = np.array(pd.read_csv(folder +'/Static_Simu/MCMC/psimu.csv', header=None))
    #print(b)
    
    #a2 = np.median(ghg,0)#.median(0)
     
    #b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    
    ta = pd.read_csv(folder + '/a.csv',index_col=0)
    tb = pd.read_csv(folder + '/b.csv',index_col=0)
    mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    
    #plt.plot(ap)
    plt.figure(figsize=(15,15))
    plt.subplot(3,2,1)
    for i in range(L):
        plt.plot(a[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(ta)[i],ls='--', color= cols[i])
    plt.title('A_LOADING', fontsize=15)
    #plt.show()

    #plt.subplot(2,2,2)
    #for i in range(2):
    #    plt.plot(deltag[:,i],color=cols[i], alpha=0.5)
    #    plt.axhline(delta[i],ls='--', color= cols[i] )
    #plt.title('Delta', fontsize=15)
    #plt.show()

    plt.subplot(3,2,3)
    #plt.plot(bp1)
    for i in range(L-1):
        plt.plot(b[:,i*2],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    plt.title('B_LOADING_1', fontsize=15)
    #plt.show()

    plt.subplot(3,2,4)
    #plt.plot(bp2)
    for i in range(L-1):
        plt.plot(b[:, i*2+1],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,1]),ls='--', color=cols[i])
    plt.title('B_LOADING_2', fontsize=15)    
   
    plt.subplot(3,2,5)
    for i in range(L):
        plt.plot(mugamg[:, i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mugam)[i],ls='--', color=cols[i])
    plt.title('a_h', fontsize=15)    

    plt.subplot(3,2,6)
    for i in range(L-1):
        plt.plot(mupsig[:, i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mupsi)[i],ls='--', color=cols[i])
    plt.title('b_h', fontsize=15)    
   
    plt.savefig('Simu_Static_Nprod10.png')
    plt.show()
    
    #plt.title('B_LOADING_1', fontsize=15)
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.hist(a[:,i])
        plt.axvline(np.array(np.array(ta)[i]),ls='--', color= cols[i])
    #plt.savefig('Simu_S3_100_A_Loading.png')
    plt.show()
    
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.hist(b[:,i*2],color=cols[i], alpha=0.5)
        plt.axvline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    #plt.title('B_LOADING_1', fontsize=15)
    #plt.savefig('Simu_S3_100_B_loading1.png')
    plt.show()

    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.hist(b[:,i*2+1],color=cols[i], alpha=0.5)
        plt.axvline(np.array(tb)[i,1],ls='--', color= cols[i])
    #plt.title('A_LOADING', fontsize=15)
    #plt.savefig('Simu_S3_100_B_loading2.png')
    plt.show()
    #return a,b, deltag, mugamg, mupsig#, a2, b2


ab3('sims7/s2')
12
(100000, 12)
In [1531]:
import glob

demands = glob.glob("yogurt/demand/*")
trips = []
for demand in demands:
    trips.append(pd.read_csv(demand).shape[0])
In [512]:
## dat = np.load('sims6/s1' + '/SSDF_Simu.npz')

def ab4(folder, c=True):
    #dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = ghg
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = bhg
    deltag = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/phig.csv', header=None))
    L = ghg.shape[1]
    #print(L)
    print(a.shape)
    
    mugamg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/gammu.csv', header=None))
    mupsig = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/psimu.csv', header=None))
    try:
        delta = np.array(pd.read_csv(folder + '/delta_bar.csv',header=None, delimiter=' ')).reshape(-1)
    except:
        delta = [1, -1]
    #print(delta.shape)
    
    #a2 = np.median(ghg,0)#.median(0)
     
    #b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    
    #ta = pd.read_csv(folder + '/a.csv',index_col=0)
    #tb = pd.read_csv(folder + '/b.csv',index_col=0)
    #mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    #mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    try:
        ta = pd.read_csv(folder + '/a.csv',index_col=0)
        tb = pd.read_csv(folder + '/b.csv',index_col=0)
        mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
        mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    except:
        ta = pd.read_csv('sims6'+ '/a.csv',index_col=0)
        tb = pd.read_csv('sims6' + '/b.csv',index_col=0)
        mugam = pd.read_csv('sims6' + '/mugam.csv',index_col=0)
        mupsi = pd.read_csv('sims6' + '/mupsi.csv',index_col=0)
    
    #plt.plot(ap)
    plt.figure(figsize=(20,15))
    plt.subplot(3,2,1)
    for i in range(L):
        plt.plot(a[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(ta)[i],ls='--', color= cols[i])
    plt.title('A_LOADING', fontsize=15)
    #plt.show()

    plt.subplot(3,2,2)
    for i in range(2):
        plt.plot(deltag[:,i],color=cols[i], alpha=0.5)
        plt.axhline(delta[i],ls='--', color= cols[i] )
    plt.title('Delta', fontsize=15)
    #plt.show()

    plt.subplot(3,2,3)
    #plt.plot(bp1)
    for i in range(L-1):
        plt.plot(b[:-1,i*2],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    plt.title('B_LOADING_1', fontsize=15)
    #plt.show()

    plt.subplot(3,2,4)
    #plt.plot(bp2)
    for i in range(L-1):
        plt.plot(b[:-1, i*2+1],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,1]),ls='--', color=cols[i])
    plt.title('B_LOADING_2', fontsize=15)    
    
    plt.subplot(3,2,5)
    for i in range(L):
        plt.plot(mugamg[:, i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mugam)[i]  * 1,ls='--', color=cols[i])
    plt.title('a_h', fontsize=15)    

    plt.subplot(3,2,6)
    for i in range(L-1):
        plt.plot(mupsig[:-1,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mupsi)[i]  * 1,ls='--', color=cols[i])
    plt.title('b_h', fontsize=15)    
    #plt.savefig('M2_PGFIX_R1.png')
    
    plt.show()
    """
    #plt.title('B_LOADING_1', fontsize=15)
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.boxplot(a[-10000:,i])
        plt.scatter(1, np.array(ta)[i], color='r', marker='x', s = 60 )
        #plt.axvline(np.array(np.array(ta)[i]),ls='--', color= cols[i])
    #plt.savefig('Simu_S3_100_A_Loading.png')
    plt.show()
    
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.boxplot(mupsig[-10000:,i] )
        plt.scatter(1, np.array(mupsi)[i] * 0, color='r', marker='x', s = 60 )
        #plt.axvline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    plt.suptitle('b_h', fontsize=15)
    plt.tight_layout()
    #plt.savefig('Simu_S2_E5_b_h.png')
    plt.show()

    for i in range(L):
        plt.subplot(4,4,i+1)
        plt.boxplot(mugamg[-10000:,i])
        plt.scatter(1, np.array(mugam)[i] * 0, color='r', marker='x', s = 60 )
        #plt.axvline(np.array(tb)[i,1],ls='--', color= cols[i])
    plt.suptitle('a_h', fontsize=15)
    plt.tight_layout()

    #plt.savefig('Simu_S2_E5_a_h.png')
    plt.show()
    #return a,b, deltag, mugamg, mupsig#, a2, b2
    """

# S3, S4, S7 <- sigma 0.1
# S9, S5 <- Fix
# S2 <- var 0.1
#3

ab4('sims6/s6' , True)
#ab4('sims6/archive/archive0628/s3', True)
(523, 10)
In [162]:
print("######### HOUSEHOLD 1000")
ab4('sims6/s2' , True)

print("######## HOUSEHOLD 400")
ab4('sims6/s3' , True)
######### HOUSEHOLD 1000
(5375, 10)
######## HOUSEHOLD 400
(3706, 10)
In [455]:
bhg = np.array(pd.read_csv('sims6/s10' +'/SSDF_Simu/MCMC/bhg.csv', header=None))
b = bhg
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-455-6084ece0d5eb> in <module>
----> 1 bhg = np.array(pd.read_csv('sims6/s10' +'/SSDF_Simu/MCMC/bhg.csv', header=None))
      2 b = bhg

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    700                     skip_blank_lines=skip_blank_lines)
    701 
--> 702         return _read(filepath_or_buffer, kwds)
    703 
    704     parser_f.__name__ = name

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    427 
    428     # Create the parser.
--> 429     parser = TextFileReader(filepath_or_buffer, **kwds)
    430 
    431     if chunksize or iterator:

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
    893             self.options['has_index_names'] = kwds['has_index_names']
    894 
--> 895         self._make_engine(self.engine)
    896 
    897     def close(self):

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
   1120     def _make_engine(self, engine='c'):
   1121         if engine == 'c':
-> 1122             self._engine = CParserWrapper(self.f, **self.options)
   1123         else:
   1124             if engine == 'python':

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
   1851         kwds['usecols'] = self.usecols
   1852 
-> 1853         self._reader = parsers.TextReader(src, **kwds)
   1854         self.unnamed_cols = self._reader.unnamed_cols
   1855 

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()

FileNotFoundError: [Errno 2] File b'sims6/s10/SSDF_Simu/MCMC/bhg.csv' does not exist: b'sims6/s10/SSDF_Simu/MCMC/bhg.csv'
In [51]:
b1 = b[:,[i*2 for i in range(9)]]

plt.plot(b1.sum(1))
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-51-c0ccc4de5548> in <module>
----> 1 b1 = b[:,[i*2 for i in range(9)]]
      2 
      3 plt.plot(b1.sum(1))

NameError: name 'b' is not defined
In [54]:
np.exp(-2)
Out[54]:
0.1353352832366127
In [659]:
# dat = np.load('sims6/s1' + '/SSDF_Simu.npz')

def ab4(folder, c=True):
    #dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = ghg
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = bhg
    deltag = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/phig.csv', header=None))
    L = ghg.shape[1]
    print(L)
    print(a.shape)
    
    mugamg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/gammu.csv', header=None))
    mupsig = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/psimu.csv', header=None))
    try:
        delta = np.array(pd.read_csv(folder + '/delta_bar.csv',header=None, delimiter=' ')).reshape(-1)
    except:
        delta = [1, -1]
    #print(delta.shape)
    
    #a2 = np.median(ghg,0)#.median(0)
     
    #b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    
    #ta = pd.read_csv(folder + '/a.csv',index_col=0)
    #tb = pd.read_csv(folder + '/b.csv',index_col=0)
    #mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    #mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    try:
        ta = pd.read_csv(folder + '/a.csv',index_col=0)
        tb = pd.read_csv(folder + '/b.csv',index_col=0)
        mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
        mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    except:
        ta = pd.read_csv('sims6'+ '/a.csv',index_col=0)
        tb = pd.read_csv('sims6' + '/b.csv',index_col=0)
        mugam = pd.read_csv('sims6' + '/mugam.csv',index_col=0)
        mupsi = pd.read_csv('sims6' + '/mupsi.csv',index_col=0)
    
    #plt.plot(ap)
    plt.figure(figsize=(20,15))
    plt.subplot(3,2,1)
    
    
    plt.boxplot(a)
    plt.plot(ta,ls='',marker="o",markersize=10, color="red")
    #for i in range(L):
    #    plt.boxplot(ap[:,i],color=cols[i])
    #    plt.axhline(np.array(a)[i],ls='--', color= cols[i] )
    plt.title('A_LOADING', fontsize=15)
    #plt.show()


    plt.subplot(3, 2, 3)
    #plt.plot(bp1)
    plt.boxplot(b[:-1,[i*2 for i in range(L-1)]])
    plt.plot(tb.iloc[:-1,0],ls='',marker="o",markersize=10, color="red")
    plt.title('B_LOADING_1', fontsize=15)
    #plt.show()

    plt.subplot(3, 2, 4)
    plt.boxplot(b[:-1,[i*2 + 1 for i in range(L-1)]])
    plt.plot(tb.iloc[:-1,1],ls='',marker="o",markersize=10, color="red")
    plt.title('B_LOADING_2', fontsize=15)
    
    plt.subplot(3,2,5)
    plt.boxplot(mugamg)
    plt.plot(mugam,ls='',marker="o",markersize=10, color="red")
    plt.title('a_h', fontsize=15)

        
    plt.subplot(3,2,6)
    plt.boxplot(mupsig)
    plt.plot(mupsi,ls='',marker="o",markersize=10, color="red")

    plt.title('b_h', fontsize=15)    
    plt.savefig('S2_delta_0.3_0.1_FIX.png')
    
    plt.show()
    
    #plt.title('B_LOADING_1', fontsize=15)
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.boxplot(a[-10000:,i])
        plt.scatter(1, np.array(ta)[i], color='r', marker='x', s = 60 )
        #plt.axvline(np.array(np.array(ta)[i]),ls='--', color= cols[i])
    #plt.savefig('Simu_S3_100_A_Loading.png')
    plt.show()
    
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.boxplot(mupsig[-10000:,i])
        plt.scatter(1, np.array(mupsi)[i], color='r', marker='x', s = 60 )
        #plt.axvline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    plt.suptitle('b_h', fontsize=15)
    plt.tight_layout()
    #plt.savefig('Simu_S2_E5_b_h.png')
    plt.show()

    for i in range(L):
        plt.subplot(4,4,i+1)
        plt.boxplot(mugamg[-10000:,i])
        plt.scatter(1, np.array(mugam)[i], color='r', marker='x', s = 60 )
        #plt.axvline(np.array(tb)[i,1],ls='--', color= cols[i])
    plt.suptitle('a_h', fontsize=15)
    plt.tight_layout()

    #plt.savefig('Simu_S2_E5_a_h.png')
    plt.show()
    #return a,b, deltag, mugamg, mupsig#, a2, b2
    

# S3, S4, S7 <- sigma 0.1
# S9, S5 <- Fix
# S2 <- var 0.1

ab4('sims6/s2' , True)
#ab4('sims6/archive0408/s9', True)
10
(139591, 10)
In [2086]:
np.median(trips)
Out[2086]:
11.0
In [1534]:
trips
Out[1534]:
[27,
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In [1244]:
plt.hist(trips,bins=50)
plt.text(30,60,"Means=%.2f" % np.mean(trips),fontsize=20)
plt.show()

#400
#10
#385
In [1227]:
plt.hist(trips,bins=50)
plt.text(30,60,"Means=%.2f" % np.mean(trips),fontsize=20)
plt.show()

#385
In [1220]:
 
Out[1220]:
17047000300 17047000302 17047000310 17047000321 17047000323 17047000636 17047000643 17047000651 17047000652 17047000655 ... 33663200660 34114800901 44114800910 44114800935 44114800945 8839999874981 8839999875035 8839999875188 8839999875289 8839999875341
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26 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 5 0 0 0

27 rows × 35 columns

In [1089]:
plt.plot(bp1)
for i in range(L):
    plt.axhline(np.array(np.array(b)[i,0]),ls='--')
plt.show()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1089-1da9cc9115c5> in <module>
----> 1 plt.plot(bp1)
      2 for i in range(L):
      3     plt.axhline(np.array(np.array(b)[i,0]),ls='--')
      4 plt.show()

NameError: name 'bp1' is not defined
In [379]:
path = 'sims6/s2'
dat = np.load(path + '/SSDF_Simu.npz', allow_pickle=True)
In [160]:
dat['fa'][60]
#dat['betaa'][:100]
Out[160]:
array([-1.39654848])
In [380]:
dat['betaa']
Out[380]:
array([[-0.18910366,  0.16359117],
       [-0.3766177 ,  0.07538865],
       [-0.46146309,  0.27829581],
       [-0.26061648, -0.00380231],
       [ 0.11914042,  0.17255894],
       [-0.52378827, -0.03172004],
       [-0.03107457,  0.16135693],
       [ 0.007212  ,  0.16291433],
       [-0.57554208,  0.03704043],
       [-0.19902573,  0.19057024],
       [-0.4089911 ,  0.3086409 ],
       [ 0.01853303,  0.21899384],
       [-0.50385212,  0.19047136],
       [-0.24007107,  0.19887971],
       [-0.1765831 ,  0.11033551],
       [-0.34239838,  0.22352918],
       [-0.41771596,  0.20038653],
       [ 0.08986752,  0.21318057],
       [-0.08123876,  0.08628712],
       [-0.35819252,  0.1519515 ],
       [-0.28994681,  0.19926285],
       [-0.51499795, -0.18140311],
       [-0.1325705 ,  0.3002864 ],
       [ 0.04492864,  0.26420327],
       [-0.38760283,  0.16710701],
       [-0.56121985, -0.01564833],
       [ 0.11463978,  0.22857358],
       [-0.19479042,  0.10272075],
       [-0.00260345, -0.0622441 ],
       [-0.37565157,  0.01921383],
       [-0.2110519 , -0.04030738],
       [-0.34851174,  0.14807346],
       [-0.09075303,  0.25041302],
       [-0.06673588, -0.1260248 ],
       [-0.36614986, -0.08804804],
       [-0.23555826,  0.29860403],
       [ 0.07930264,  0.0306806 ],
       [-0.31069947, -0.23379495],
       [-0.16104024,  0.01807939],
       [-0.22812006,  0.19385857],
       [-0.20781216,  0.1339394 ],
       [-0.25558032,  0.28167904],
       [-0.52381852,  0.42333507],
       [-0.31699496,  0.06748176],
       [-0.32617011,  0.08573527],
       [-0.04769284,  0.10067312],
       [-0.61628842,  0.08656609],
       [-0.21095195,  0.24923186],
       [-0.39528303, -0.01924525],
       [-0.37977158,  0.44406921],
       [-0.4702903 , -0.11798016],
       [-0.17071196, -0.08427068],
       [-0.20269577, -0.23131015],
       [-0.25053492,  0.63817609],
       [-0.27662079,  0.31503127],
       [-0.14760856,  0.13666234],
       [-0.70223844,  0.17206767],
       [ 0.09194606,  0.20149927],
       [-0.43384589, -0.08256082],
       [-0.38965246, -0.01935935],
       [-0.3391376 ,  0.04051075],
       [-0.18815921,  0.4815437 ],
       [-0.46942296,  0.17147451],
       [-0.38990476, -0.18438827],
       [-0.55286097,  0.23224338],
       [-0.26820441,  0.1423375 ],
       [-0.34962235,  0.27920404],
       [-0.38843326, -0.04715602],
       [-0.16723172,  0.34637414],
       [-0.42582908,  0.0559338 ],
       [-0.35902826,  0.26088743],
       [-0.24904813,  0.21411106],
       [-0.37299669,  0.15088976],
       [-0.20639724,  0.04782595],
       [-0.18024392,  0.22124916],
       [-0.35768027,  0.31489697],
       [-0.40233423,  0.21595116],
       [-0.25901537,  0.1912221 ],
       [-0.22141692, -0.00890523],
       [-0.28274617,  0.2069607 ],
       [-0.5634024 , -0.17151987],
       [-0.39449627,  0.13722532],
       [-0.2841918 ,  0.30527532],
       [-0.13913281,  0.40378478],
       [-0.41826031,  0.22141158],
       [-0.3584338 ,  0.42251559],
       [-0.20290152,  0.17694839],
       [-0.01987733,  0.37419597],
       [ 0.08231515,  0.0093185 ],
       [-0.01070395, -0.02843664],
       [-0.14717266, -0.04943482],
       [ 0.00858222,  0.042525  ],
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       [-0.27413104,  0.0786066 ],
       [-0.16172198,  0.27286393],
       [-0.27724278, -0.0630361 ],
       [-0.38069624,  0.060421  ],
       [-0.21422306,  0.14976339],
       [-0.14831524,  0.11637655],
       [-0.2198599 , -0.23724412],
       [-0.38950477,  0.04723696],
       [-0.16374132,  0.19898758],
       [-0.45679608, -0.09748185],
       [-0.09410306,  0.27435115],
       [-0.36416634, -0.03672618],
       [-0.32626969,  0.03798855],
       [-0.38950269, -0.1399682 ],
       [-0.46373646, -0.09583834],
       [-0.40320564, -0.02342329],
       [-0.31186681,  0.07881734],
       [-0.73378957,  0.1497937 ],
       [-0.33537686,  0.05616653],
       [-0.50660319,  0.1028437 ],
       [ 0.02686535,  0.21515873],
       [-0.09924754, -0.03647663],
       [-0.28715383,  0.51834884],
       [-0.27018725, -0.21955629],
       [-0.17256341,  0.3165495 ],
       [-0.21269978,  0.18548772],
       [-0.07796073,  0.13083161],
       [-0.23383721,  0.13738374],
       [-0.25991385,  0.15367237],
       [ 0.08389083,  0.01644694],
       [ 0.24414623,  0.11162775],
       [-0.10556206,  0.0712235 ],
       [-0.31899745,  0.42497975],
       [-0.23720638,  0.18621866],
       [-0.32706303,  0.19843991],
       [-0.23006162,  0.0520718 ],
       [-0.0966481 ,  0.02492394],
       [-0.26185934,  0.09822499],
       [-0.4021477 , -0.04864771],
       [-0.4802436 ,  0.29910697],
       [-0.05195196,  0.27578828],
       [-0.51446306,  0.3429109 ],
       [-0.3219815 ,  0.11458303],
       [-0.29092757,  0.31668113],
       [-0.2998518 , -0.02276755],
       [-0.37302749,  0.23041129],
       [-0.55309347,  0.09337851],
       [-0.28844355, -0.03677808],
       [-0.25425103,  0.199999  ],
       [-0.50343239,  0.09958159],
       [-0.2327876 ,  0.09251126],
       [-0.42882302,  0.34112226],
       [-0.20905448,  0.13312537],
       [-0.34458963,  0.2829506 ],
       [-0.13208243,  0.16082606],
       [-0.49313901,  0.0819044 ],
       [-0.59648488,  0.16240489],
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       [-0.25016669,  0.10175925],
       [-0.62723265,  0.05926877],
       [-0.33478801,  0.0777727 ],
       [-0.49531583,  0.18546668],
       [-0.35038041,  0.22482024],
       [-0.1561334 ,  0.29985998],
       [-0.50853941,  0.0801984 ],
       [-0.52404567,  0.13401592],
       [ 0.15496392, -0.02337365],
       [-0.39991549, -0.01786399],
       [-0.3313126 ,  0.05703875],
       [-0.21449992,  0.10360895],
       [-0.18548132,  0.05705832],
       [-0.29899996,  0.02565824],
       [-0.51494375,  0.47528043],
       [-0.35095098,  0.26318607],
       [-0.62406231,  0.37537627],
       [-0.22920239,  0.40094091],
       [-0.10424556,  0.07495911],
       [-0.14944758,  0.06951429],
       [-0.56211882,  0.13180134],
       [-0.1094039 , -0.13109437],
       [-0.27299225,  0.33130238],
       [-0.64155819,  0.16243848],
       [-0.07959755,  0.02713565],
       [-0.22673219,  0.06388783],
       [-0.23396199,  0.17734945],
       [-0.03349857, -0.02192665],
       [-0.00620153,  0.117219  ],
       [-0.32188747,  0.20501319],
       [-0.1907684 ,  0.42060333],
       [-0.48160456,  0.16895827],
       [-0.18681727,  0.53165341],
       [-0.22031781,  0.33771399],
       [-0.56789507,  0.11664728],
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       [ 0.07684063,  0.26013706],
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       [-0.20435075,  0.1616221 ],
       [-0.1708871 ,  0.11550866],
       [-0.44573367,  0.20408   ],
       [-0.31724842,  0.13866594],
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       [-0.08599594,  0.63866739],
       [-0.193704  ,  0.23606068],
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       [-0.2128998 ,  0.32321525],
       [-0.21920675,  0.31938419],
       [-0.21742235, -0.10936433],
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       [-0.00259883,  0.19790569],
       [-0.27119271,  0.39124685],
       [-0.26328892,  0.27359038],
       [-0.22438974,  0.37529027],
       [-0.48283371, -0.02774432],
       [-0.57724045,  0.09857106],
       [ 0.11805893,  0.28529896],
       [-0.11073898,  0.17927323],
       [-0.23899555,  0.29083632],
       [-0.04454384,  0.09103987],
       [-0.24981876,  0.28342304],
       [-0.2863084 ,  0.22767254],
       [-0.30939472,  0.0604341 ],
       [-0.36111059,  0.08050669],
       [-0.34838578,  0.503432  ],
       [-0.355645  ,  0.35464874],
       [-0.30771262,  0.13381445],
       [-0.11748974, -0.1631947 ],
       [-0.02191996,  0.31319348],
       [-0.02368935,  0.0823713 ],
       [-0.13023912,  0.30304634],
       [-0.28127086, -0.01430719],
       [-0.26277613, -0.32125771],
       [-0.42268519, -0.07062699],
       [-0.13141341,  0.14598145],
       [-0.12102954,  0.32574148],
       [-0.30433365,  0.43833278],
       [-0.13808326,  0.04042023],
       [-0.14129429,  0.02819096],
       [-0.23229765,  0.3831271 ],
       [-0.06949759,  0.05363247],
       [-0.22445207,  0.11066545],
       [ 0.04176256, -0.05199301],
       [-0.40695845,  0.04476697],
       [-0.3264985 ,  0.19832641],
       [-0.27889872,  0.05352708],
       [-0.43061011,  0.13984802],
       [-0.68338235,  0.24441537],
       [-0.39203867,  0.11562864],
       [-0.10792533, -0.17145571],
       [-0.08177032,  0.34833311],
       [-0.11672975, -0.02324382],
       [-0.23149911,  0.1955087 ],
       [-0.5290195 ,  0.33848811],
       [-0.15161197,  0.18324982],
       [-0.44295763,  0.12009329],
       [-0.16484396, -0.31316495],
       [-0.21772483,  0.03032068],
       [-0.4309577 ,  0.16484353],
       [-0.11717059,  0.25595217],
       [-0.3668172 ,  0.21258377],
       [-0.49210797, -0.06505829],
       [-0.58739376,  0.0609466 ],
       [-0.58467905,  0.05254241],
       [-0.3212024 ,  0.37113528],
       [-0.32541179, -0.14949299],
       [-0.54231925, -0.01630601],
       [-0.40277246, -0.11000696],
       [-0.03408983,  0.17366758],
       [-0.61232999,  0.11275434],
       [-0.323942  , -0.05221843],
       [ 0.00157824,  0.13506811],
       [-0.56121297,  0.17787685],
       [-0.26539573,  0.33652615],
       [-0.44582801, -0.01361264],
       [-0.19084504,  0.34370399],
       [-0.22215408,  0.15711958],
       [-0.39468674, -0.13433574],
       [-0.35948281,  0.00911663],
       [-0.25956235,  0.09134437],
       [-0.13240873, -0.00819658],
       [-0.30287686,  0.54233013],
       [-0.0945242 , -0.12918554],
       [-0.35807527, -0.02103627],
       [-0.21294176,  0.10132025],
       [-0.11454495,  0.16490737],
       [-0.14404698,  0.05148148],
       [-0.35007711,  0.28312966],
       [-0.0077052 ,  0.32333738],
       [ 0.1389413 ,  0.11830915],
       [-0.23809804,  0.407103  ],
       [-0.274737  ,  0.01926841],
       [-0.19397657,  0.11598141],
       [-0.24665421,  0.15118371],
       [-0.66636415,  0.21289363],
       [-0.26918687,  0.06431881],
       [-0.12248708,  0.26502365],
       [-0.28085381,  0.29981612],
       [-0.40835149, -0.03659183],
       [-0.28061436,  0.08715536],
       [-0.50519887, -0.04588335],
       [-0.29132602,  0.28825361],
       [-0.35393632,  0.01443905],
       [-0.16423613, -0.0363583 ],
       [-0.12118142, -0.24113872],
       [-0.06662224,  0.29458367],
       [-0.46093649,  0.19741263],
       [-0.05498635,  0.12201045],
       [-0.27745723,  0.11410976],
       [-0.33879264, -0.03865922],
       [-0.50268343, -0.08610107],
       [-0.49134357, -0.07091793],
       [-0.33916039,  0.03791443],
       [-0.24401612, -0.12208075],
       [-0.48284344,  0.17732081]])
In [ ]:
 
In [697]:
dat['bh']
Out[697]:
array([[ 1.        ,  0.        ],
       [ 0.91647734,  1.        ],
       [-0.28388067, -0.28590201],
       [-0.02572564,  0.08194979],
       [ 0.09010574,  0.20101297]])
In [324]:
#path = 'sims6/archive0525/s6'
path = 'sims6/s3'
dat = np.load(path + '/SSDF_Simu.npz', allow_pickle=True)
L = np.array(pd.read_csv(path +'/SSDF_Simu/MCMC/ghg.csv', header=None)).shape[0] 
L = L - L %100

testpsi = dat["psi"]#(dat['psim']/L)
testgam = dat["gamma"]#(dat['gamm']/L)

truepsi = pd.read_csv(path +'/psis.csv', index_col=0)
truegam = pd.read_csv(path +'/gams.csv', index_col=0)

plt.figure(figsize=(15,10))

plt.subplot(1,2,1)
bins = 100
plt.hist(testgam.reshape(-1),bins, alpha=0.5,color='b',range=(-30,30))

plt.hist(np.array(truegam).reshape(-1),bins, alpha=0.5, color='r',range=(-30,30))
plt.legend(["Est","True"],fontsize=20)
plt.title("Gamma", fontsize=20)
#plt.show()


plt.subplot(1,2,2)
plt.hist(testpsi.reshape(-1),bins, alpha=0.5,color='b', range=(-30,30))

plt.hist(np.array(truepsi).reshape(-1),bins, alpha=0.5, color='r',range=(-30,30))
plt.title("Psi", fontsize=20)
plt.legend(["Est","True"],fontsize=20)
plt.show()

psi = likelihood * pdf(psi- g*b, 1)
psi -   g* b <- 0
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-324-d75290aeeb3b> in <module>
     31 plt.show()
     32 
---> 33 psi = likelihood * pdf(psi- g*b, 1)
     34 psi -   g* b <- 0

NameError: name 'likelihood' is not defined
In [ ]:
 
In [642]:
dat['betam']
Out[642]:
array([[ 1234.76134997, -1362.44987104],
       [ 2102.37028341, -1216.57826904],
       [ 1604.49899482, -1148.95715799],
       [ 1899.65337929, -1512.77623422],
       [ 1561.03467496, -1179.50959276],
       [ 1473.43014022, -1447.09181902],
       [ 1684.49745251, -1267.02649489],
       [ 1289.03515666, -1380.15709434],
       [ 1458.27176556, -1812.11308571],
       [ 1805.27636161, -1512.28136949],
       [ 1083.23642178, -1683.16139001],
       [ 1745.04679547, -1878.75633531],
       [ 1773.28121023, -1820.4018869 ],
       [ 1423.10206294, -1491.34438301],
       [ 1652.84375266, -1607.56935818],
       [ 1650.85205295, -1367.97400605],
       [ 1349.13624856, -1463.55994411],
       [ 1407.5249324 , -1290.20612259],
       [ 1367.15619345, -1195.76006892],
       [ 1445.24078884, -1080.87908874],
       [ 1114.70521706,  -843.96631143],
       [ 1375.9252095 , -1454.75747423],
       [ 1510.71982163, -1383.21886799],
       [ 1663.16739083, -1329.0201601 ],
       [ 1871.26764732, -1411.48865871],
       [ 1311.45846736,  -772.20486766],
       [ 1491.25517034, -1141.90164366],
       [ 1146.2846648 , -1184.3940186 ],
       [ 1364.08171135, -1129.40676315],
       [ 1093.96265177, -1465.36725005],
       [ 1331.35176414, -1665.28517467],
       [ 1451.42334422, -1246.34981583],
       [ 1696.72481205, -1474.62861892],
       [ 1416.70119961, -1155.31354827],
       [ 1447.22794421, -1353.53039023],
       [ 1706.16073038, -1133.90202449],
       [ 1664.78538617, -1345.72627624],
       [ 1142.8206596 ,  -896.1620429 ],
       [ 1139.42168585, -1248.56320845],
       [ 1334.86802812,  -866.45070542],
       [ 1548.42630742, -1387.31391091],
       [ 1169.8753909 , -1380.3422901 ],
       [ 1435.14011158, -1416.67510439],
       [ 1350.98112567, -1601.13873429],
       [ 1229.65644543, -1151.89427984],
       [ 1219.04428348, -1296.9055458 ],
       [ 1313.92526045, -1229.18795668],
       [ 1840.41000831, -1558.59904416],
       [ 1477.00980995, -1797.19204276],
       [ 1250.23113991, -1523.58102571],
       [ 1384.01927983, -1837.74143324],
       [ 1018.25405202, -1298.08518568],
       [ 1632.07710941, -2090.35316612],
       [ 1393.77792647, -1780.07453766],
       [ 1628.70611086, -1648.04083014],
       [ 1607.7252685 , -1649.58912804],
       [  793.80549245, -1680.61982437],
       [ 1131.86296073, -1427.1524884 ],
       [ 1280.39447873, -1952.29822381],
       [ 1007.23052094, -1182.34857625],
       [  730.66557821, -1530.47215935],
       [  658.2874796 , -1329.39129075],
       [ 1534.82563173, -1279.2288331 ],
       [ 1367.09828269, -1879.27080502],
       [ 1376.51185119, -1504.85285265],
       [ 1401.43277425, -1235.64620863],
       [ 1363.84433656, -1703.15214783],
       [ 1658.93452976, -1278.87617403],
       [ 1453.99861031,  -916.50176348],
       [ 1373.62334746, -1520.0172938 ],
       [ 1180.29811453, -1555.61807493],
       [ 1319.95558505, -1630.44246184],
       [ 1647.51085384, -1739.62348587],
       [ 1186.15278263, -2203.89120047],
       [ 1309.83445605, -1293.06054921],
       [ 1801.15893903, -1341.79504279],
       [ 1449.28717778, -1624.36611551],
       [ 1611.37158101, -1619.17608399],
       [ 1246.58844617, -1528.7382805 ],
       [ 1404.74520265, -1193.42056769],
       [ 1493.63849832, -1335.66083532],
       [ 1383.14357409, -1338.10058067],
       [ 1059.89320681, -1531.77826405],
       [ 1115.25243409, -1370.81449369],
       [ 1162.88970628, -1231.82358101],
       [  999.85498692, -1183.20009854],
       [ 1491.99173655, -1317.52486003],
       [ 1319.88021489, -1471.51719304],
       [ 1397.21448529, -1005.97353253],
       [ 1164.60654378, -1290.81920626],
       [ 1483.8196067 , -1636.51853044],
       [ 1337.47446582,  -906.82777549],
       [ 1537.11552551, -1606.50864846],
       [ 1305.05260077, -1452.48931783],
       [ 1500.79996776, -1823.94510985],
       [ 1503.37314485, -1745.70857482],
       [ 1316.4807484 , -1404.52832365],
       [ 1232.72689744, -1509.03820176],
       [ 1357.56339845, -1004.39086886],
       [ 1483.15584126, -1329.36399868],
       [ 1328.44656633, -1726.57194972],
       [ 1091.87250134, -1474.50163893],
       [ 1738.2786052 , -1239.20703012],
       [ 1244.60936778, -1392.43989299],
       [ 1842.51542299, -1677.80251513],
       [ 1438.16452823, -1843.09324233],
       [  630.3133983 , -1649.84515032],
       [ 1503.43418279, -1371.9431502 ],
       [ 1673.68563752, -1489.64922232],
       [ 1015.99711314, -1474.11552244],
       [ 1508.12564199, -1721.33862854],
       [ 1425.07272493, -1438.88670869],
       [ 1449.49067401, -1504.71921458],
       [ 1654.694957  , -1517.25786715],
       [ 1255.75883245, -1398.42688636],
       [ 1496.84454894, -1007.60878349],
       [ 1557.66639077, -1816.49609924],
       [ 1373.96592707, -1719.49671989],
       [ 1281.72416116, -1501.28902534],
       [ 1468.33599895, -1700.63601802],
       [ 1453.08268638, -1456.76352464],
       [ 1293.00684695, -1215.758531  ],
       [ 1594.30779148, -1099.25778732],
       [ 1515.80601454, -2188.56129357],
       [ 1388.45091742, -1381.42039616],
       [  917.23912063, -1532.15959062],
       [ 1088.22263699, -1412.45894392],
       [ 1343.34555677, -1067.36062348],
       [ 1270.30139122, -1427.38487292],
       [ 1532.68049311, -1270.19197438],
       [ 1482.65056024,  -970.50272737],
       [ 1811.36423123, -1269.17655802],
       [ 1230.70797056, -1195.00609568],
       [ 1361.75166832, -1248.79436228],
       [ 1019.14333714, -1180.38755803],
       [ 1416.06744393, -1420.03052505],
       [ 1416.04525829, -1300.4251818 ],
       [ 1453.37385269, -1658.5262964 ],
       [ 1860.29467469, -1586.99264928],
       [ 1002.60982888, -1709.64740485],
       [ 1813.17106975, -1218.3557118 ],
       [ 1277.84018645, -1662.88917528],
       [ 1842.95666435, -1451.59186719],
       [ 1196.44725524, -1135.53681034],
       [ 1717.69598257, -1320.98703793],
       [ 1654.19713993, -1039.89375333],
       [ 1688.74882258, -1317.66786468],
       [ 1204.41624156, -1575.8915014 ],
       [  783.14795069, -1057.28816768],
       [  976.73204399, -1420.90422388],
       [ 1621.50212237, -1428.98542654],
       [ 1313.20194757, -1029.65379981],
       [ 1754.19453728,  -677.65507799],
       [ 1084.98057837,  -737.17310935],
       [ 1681.81566023, -1730.41148762],
       [ 1426.13634756, -1528.02907735],
       [ 1303.93439378, -1356.17051965],
       [ 1245.38041685, -1200.42606359],
       [  954.69099573, -1736.76036743],
       [ 1439.5588674 , -1416.96703834],
       [ 1009.23347503, -1231.84867539],
       [  836.9107027 , -1139.1570169 ],
       [ 1762.70632769, -1262.53759038],
       [ 1262.37948427, -1325.12534891],
       [ 1616.80453902, -1582.20934352],
       [ 1256.5920515 , -1224.58273012],
       [ 1483.65507235, -1421.26723181],
       [ 1158.01281178, -1165.06446444],
       [ 1427.88973174, -1564.10037071],
       [ 1018.40935059, -1222.60802552],
       [ 1707.65681371, -1566.56761936],
       [ 1598.90182396, -1205.48067314],
       [ 1413.84913577, -1414.23477421],
       [ 1006.57690349, -1249.87328034],
       [ 1119.40852488, -1536.36131068],
       [ 1779.64659461, -1670.12044369],
       [  978.39169968, -1602.66213577],
       [ 1362.68267854, -1370.57016505],
       [ 1331.20667157, -1163.39395354],
       [ 1389.15269198, -1745.40142208],
       [ 1181.3358593 , -1262.68273593],
       [ 1068.08899039, -1458.77645443],
       [  903.61707165, -1540.76753451],
       [ 1417.29912859, -1229.15800337],
       [ 1957.48923602, -1257.98301696],
       [ 1858.92214417, -1526.50566789],
       [ 1812.06487496, -1423.78708934],
       [ 1066.26682518, -1323.44379008],
       [  883.40947876, -1450.63502147],
       [ 1337.36319052, -1449.77570233],
       [ 1584.19199659, -1126.5361738 ],
       [ 2016.47759697, -1550.75747843],
       [  979.66698168, -1939.99088059],
       [ 1315.14312265, -1019.03139631],
       [ 1425.5230326 , -1292.89049578],
       [ 1105.47078095, -1631.57828885],
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       [ 1493.56110851,  -926.08181502],
       [ 1039.17804332, -1635.45736364],
       [ 1431.17311694, -1543.88370494],
       [ 1411.04794844, -1052.62037881],
       [ 1091.83267744, -1219.29404565],
       [ 1356.58597177, -1498.06546198],
       [ 2026.37645302, -1797.44213695],
       [ 1043.66395904, -1359.54431967],
       [ 1318.64996085, -1400.43319831],
       [ 1588.77879808,  -957.9230387 ],
       [ 1269.88341447, -2044.83573333],
       [ 1703.22367717, -1447.45717915],
       [ 1419.61832531, -1642.64150213],
       [ 1304.39843717, -1569.34915589],
       [ 1246.00543647, -1618.0923579 ],
       [ 1257.55896372, -1369.75490487],
       [ 1469.50946216,  -943.81283448],
       [ 1892.63332133, -1481.5015945 ],
       [ 1647.63947236, -1608.97389026],
       [ 1530.67051746, -1364.36473037],
       [  959.48046427, -1283.76588932],
       [  972.97061848, -1217.45796649],
       [ 2005.38443278, -1720.58693653],
       [ 1149.88750711, -1139.3429178 ],
       [ 1197.33836891, -1480.95048545],
       [ 1197.65210987, -2284.65940063],
       [ 1057.88389063, -1119.2260717 ],
       [  991.18487785, -1000.79885366],
       [ 1374.09597963, -1964.00449821],
       [ 1426.41880459, -1577.92359639],
       [ 1554.09613734, -1437.17347082],
       [ 1540.05960956, -1451.43013081],
       [ 1894.30445507, -1353.60462879],
       [ 1295.6023926 , -1434.63992706],
       [ 1900.6970462 , -1509.80366266],
       [ 1471.75394057, -1348.24332864],
       [ 1421.6712249 , -1525.87742454],
       [ 1231.57914342,  -788.10950378],
       [ 1312.97064122, -1458.79656657],
       [ 1555.73314562, -1342.76388925],
       [ 1420.64958615, -1332.80444804],
       [ 1767.90322452, -1465.34130364],
       [  854.28418198, -1348.53058143],
       [ 1252.23511861, -1207.42587265],
       [ 1474.45279637, -1373.63949363],
       [ 1140.48849482, -1476.94659289],
       [  990.77728994, -1238.80245762],
       [ 1384.02829859, -1474.64884555],
       [  851.88123752, -1254.58867046],
       [ 1654.01925359, -1509.57548526],
       [ 1162.29304514,  -997.05890801],
       [ 1230.15813965, -1418.5156987 ],
       [ 1107.43713542, -1022.79376363],
       [ 1771.2900482 , -1834.43366394],
       [ 1524.37310314, -1474.97819292],
       [ 1186.87528902, -1651.33729994],
       [ 1605.68915748, -1120.61832767],
       [ 1515.78745812, -1964.99352186],
       [ 1879.38433399, -1305.78761976],
       [  954.88660627, -1267.15033861],
       [ 1378.74655453, -1870.58512716],
       [ 1459.3954483 , -1534.28079836],
       [ 1041.98452026,  -919.05589911],
       [ 1199.33650506, -1658.64300695],
       [ 1694.40726213,  -901.51489064],
       [  726.86996552, -1500.37451283],
       [ 1324.20693368, -1576.64470329],
       [ 1741.6384909 , -1499.71723972],
       [ 1001.66036578, -1254.84848505],
       [ 1423.22456836, -1604.46658317],
       [ 1630.70853796, -1412.50219978],
       [ 1534.456114  , -1148.19253652],
       [ 1407.53568724, -1637.48288875],
       [ 1534.30173342,  -930.369892  ],
       [ 1626.39435631, -1045.10861116],
       [ 1218.96333234, -1685.41547937],
       [ 1915.81337892, -1256.60156837],
       [ 1319.61490891, -1985.25397929],
       [ 1667.28733037, -1542.89311013],
       [ 1258.16254027, -1050.64197861],
       [ 1454.95199859, -1567.67350508],
       [ 1235.21989536, -1619.10611302],
       [ 1373.05979892, -1117.32075496],
       [ 1348.53407441, -1563.60609968],
       [ 1198.39228141, -1502.40473948],
       [ 1326.56553183,  -615.06496569],
       [ 1787.33441155, -1316.88588983],
       [  869.72373979, -1467.83569476],
       [ 1467.93882818, -1483.16692246],
       [ 1754.88225719, -2038.07046626],
       [ 1167.06894401, -1141.12844459],
       [ 1789.04503209, -1328.77668466],
       [ 1266.87419057,  -889.8207778 ],
       [ 1548.26523526, -1659.91279285],
       [ 1276.27113484, -1377.39276368],
       [ 2183.49427175, -1932.47743649],
       [ 1559.91565005, -1405.39199644],
       [ 1651.94504702, -1948.9030628 ],
       [ 1256.30637245, -1546.26586444],
       [ 1331.25800926, -2064.71932   ],
       [ 1716.25328282, -1634.103686  ],
       [ 1295.2966579 , -1334.6796225 ],
       [ 2157.04936858, -1138.46208647],
       [ 1030.91474928, -2029.96987477],
       [ 1687.58767537, -1603.18189267],
       [ 1375.26174057, -1117.85384864],
       [ 1611.1603184 , -1769.77250255],
       [ 1403.08508686, -1131.5107383 ],
       [ 1198.01789957, -1476.25612593],
       [ 1141.60479823, -1303.95779592],
       [ 1065.77448574, -1900.88937228],
       [ 1484.84052152, -1727.24517762],
       [ 1626.4528513 , -1265.98130891],
       [ 1645.6875329 , -1267.89592595],
       [  885.52036729, -1299.08578903],
       [ 1671.82770548, -1464.00957136],
       [  983.07576272, -1220.9491853 ],
       [ 1986.45275934, -1317.27710636],
       [ 1514.52368651, -1111.14596766],
       [ 1798.45278504, -1171.39890989],
       [ 1416.43403696, -1567.01130302],
       [ 1488.60103723, -1769.84289969],
       [ 1363.86373706, -1260.35660924],
       [ 1319.85265009, -1071.45635306],
       [ 1146.26229438, -1555.66544207],
       [ 1219.03024407, -1400.47008124],
       [ 1530.92632273, -1482.97798827],
       [ 1140.29865245, -1604.6961331 ],
       [ 1049.68756132,  -952.11460034],
       [ 1021.54149743, -1795.09394708],
       [ 1260.81249391, -1650.77933366],
       [ 1626.9760734 , -1236.48908371],
       [ 1461.87470698, -1860.15670776],
       [ 1636.70894678, -1158.41208763],
       [ 1359.24272799, -2123.43534468],
       [ 1878.0747209 , -1682.40017938],
       [ 1798.48680055, -1593.58236071],
       [ 1156.19069643, -1347.14828668],
       [  852.598074  , -1434.69543964],
       [ 1559.35380845, -1263.61686984],
       [ 1622.59971014,  -884.85460203],
       [ 1934.14650642, -1825.82667074]])
In [1861]:
testpsi = (dat['psi'])
testgam = (dat['gamma'])
In [1192]:
np.concatenate([(testpsi.mean(0)).reshape(-1,1), np.array(truepsi.mean(0)).reshape(-1,1)],1)
Out[1192]:
array([[ 0.61838448,  0.39164902],
       [-0.3376119 , -0.2779233 ],
       [ 0.37483041,  0.25748534],
       [-0.57330145, -0.41212917],
       [ 0.44133157,  0.28657614],
       [-0.27098638, -0.15587848],
       [ 0.60699493,  0.40310164],
       [ 0.38334437,  0.25305993],
       [ 0.69278303,  0.49979056],
       [-0.18860265, -0.19959161],
       [-0.26671627, -0.17236465],
       [ 0.20431539,  0.10340886]])
In [1023]:
truepsi.head(10)
Out[1023]:
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
1 0.947245 -0.147460 -2.183394 -2.486523 0.315433 -2.211356 0.465486 -0.557239 1.852736 1.121845
2 -1.574422 0.420153 -0.549424 0.101597 -0.814992 -2.117318 -1.644238 1.685014 -0.596516 0.237720
3 0.861324 -1.475928 -0.475567 2.203211 -0.178966 -3.334434 1.200345 1.029684 2.336716 2.992021
4 0.776475 0.320614 0.508240 0.726144 1.879524 -2.739938 3.213058 -1.521923 2.914453 5.090267
5 -2.107121 -2.032833 -1.786455 -1.521143 1.069847 -5.536966 -0.187949 -0.031075 1.825495 -0.179090
6 0.191146 0.201650 0.979510 0.130342 0.757292 -5.524117 0.343788 -2.846492 0.015984 1.090026
7 1.602727 0.958588 -1.427538 1.037342 1.536750 -2.355226 1.781616 0.406384 0.775349 3.751886
8 1.728331 0.692759 0.240786 1.977324 2.272485 -1.590163 4.765667 0.388211 1.204096 5.030694
9 0.922764 0.237692 1.058850 0.033125 1.177735 -0.827593 1.749459 -1.018388 -0.432524 2.903691
10 0.056929 0.129283 0.667952 0.033121 1.447998 -3.702121 0.965958 -0.761690 1.561995 2.124307
In [1862]:
truegam = pd.read_csv('sims6/s7/gams.csv', index_col=0)
In [1863]:
np.concatenate([(testgam.mean(0)).reshape(-1,1), np.array(truegam.mean(0)).reshape(-1,1)],1)
Out[1863]:
array([[ 1.0572567 ,  0.08622717],
       [-0.39664265, -0.05577704],
       [ 2.29315045,  0.25841202],
       [ 1.01563286,  0.11214121],
       [ 0.29504693,  0.02625367],
       [-0.08635067, -0.02593379],
       [ 1.79867721,  0.17001386],
       [-0.04181588, -0.01633337],
       [-0.1655875 , -0.0242436 ],
       [ 0.18602576, -0.03098342]])
In [2314]:
#pd.DataFrame(np.concatenate( [dat['fa'], dat['ga'],dat['kt'].reshape(-1,1)],1 )).iloc[:60]
In [2308]:
estab = np.concatenate([dat['fa'], dat['ga']],1)
In [734]:
tfa = np.array(pd.read_csv( "sims6/s9/f.csv"))
tga = np.array(pd.read_csv("sims6/s9/g.csv"))
In [735]:
trueab = np.concatenate([tfa,tga],1)
In [732]:
plt.figure(figsize=(10,10))
plt.hist(diffab[:,0].reshape(-1),bins, alpha=0.5,color='b', range=(-30,30))
plt.hist(diffab[:,3].reshape(-1),bins, alpha=0.5,color='r', range=(-30,30))
plt.title("f",fontsize=20)
plt.legend(["Est","True"])
#plt.hist(np.array(truepsi).reshape(-1),bins, alpha=0.5, color='r',range=(-30,30))
plt.show()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-732-eadf1b6a7a1d> in <module>
      1 plt.figure(figsize=(10,10))
----> 2 plt.hist(diffab[:,0].reshape(-1),bins, alpha=0.5,color='b', range=(-30,30))
      3 plt.hist(diffab[:,3].reshape(-1),bins, alpha=0.5,color='r', range=(-30,30))
      4 plt.title("f",fontsize=20)
      5 plt.legend(["Est","True"])

NameError: name 'diffab' is not defined
<Figure size 720x720 with 0 Axes>
In [2312]:
plt.figure(figsize=(10,10))
bins=200
plt.hist(diffab[:,1].reshape(-1),bins, alpha=0.5,color='b', range=(-30,30))
plt.hist(diffab[:,4].reshape(-1),bins, alpha=0.5,color='r', range=(-30,30))
plt.title("g1",fontsize=20)
plt.legend(["Est","True"])
#plt.hist(np.array(truepsi).reshape(-1),bins, alpha=0.5, color='r',range=(-30,30))
plt.show()
In [2329]:
L = np.array(pd.read_csv(path +'/SSDF_Simu/MCMC/ghg.csv', header=None)).shape[0] 
L = L - L %100
beta = dat['betam'] / L
beta
---------------------------------------------------------------------------
BadZipFile                                Traceback (most recent call last)
<ipython-input-2329-b4683a73fb69> in <module>
      1 L = np.array(pd.read_csv(path +'/SSDF_Simu/MCMC/ghg.csv', header=None)).shape[0]
      2 L = L - L %100
----> 3 beta = dat['betam'] / L
      4 beta

~/anaconda3/lib/python3.7/site-packages/numpy/lib/npyio.py in __getitem__(self, key)
    260                 return format.read_array(bytes,
    261                                          allow_pickle=self.allow_pickle,
--> 262                                          pickle_kwargs=self.pickle_kwargs)
    263             else:
    264                 return self.zip.read(key)

~/anaconda3/lib/python3.7/site-packages/numpy/lib/format.py in read_array(fp, allow_pickle, pickle_kwargs)
    732                     read_count = min(max_read_count, count - i)
    733                     read_size = int(read_count * dtype.itemsize)
--> 734                     data = _read_bytes(fp, read_size, "array data")
    735                     array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
    736                                                              count=read_count)

~/anaconda3/lib/python3.7/site-packages/numpy/lib/format.py in _read_bytes(fp, size, error_template)
    871         # done about that.  note that regular files can't be non-blocking
    872         try:
--> 873             r = fp.read(size - len(data))
    874             data += r
    875             if len(r) == 0 or len(data) == size:

~/anaconda3/lib/python3.7/zipfile.py in read(self, n)
    897         self._offset = 0
    898         while n > 0 and not self._eof:
--> 899             data = self._read1(n)
    900             if n < len(data):
    901                 self._readbuffer = data

~/anaconda3/lib/python3.7/zipfile.py in _read1(self, n)
    987         if self._left <= 0:
    988             self._eof = True
--> 989         self._update_crc(data)
    990         return data
    991 

~/anaconda3/lib/python3.7/zipfile.py in _update_crc(self, newdata)
    915         # Check the CRC if we're at the end of the file
    916         if self._eof and self._running_crc != self._expected_crc:
--> 917             raise BadZipFile("Bad CRC-32 for file %r" % self.name)
    918 
    919     def read1(self, n):

BadZipFile: Bad CRC-32 for file 'betam.npy'
In [2317]:
tbeta =  np.array(pd.read_csv("sims6/s6/betas.csv"))[:,1:]
In [2320]:
plt.figure(figsize=(10,10))
plt.hist(tbeta[:,0].reshape(-1),bins, alpha=0.5,color='b', range=(-2,2))
plt.hist(beta[:,0].reshape(-1),bins, alpha=0.5,color='r', range=(-2,2))
plt.title("delta_1",fontsize=20)
plt.legend(["Est","True"])
#plt.hist(np.array(truepsi).reshape(-1),bins, alpha=0.5, color='r',range=(-30,30))
plt.show()
In [2321]:
plt.figure(figsize=(10,10))
plt.hist(tbeta[:,1].reshape(-1),bins, alpha=0.5,color='b', range=(-2,2))
plt.hist(beta[:,1].reshape(-1),bins, alpha=0.5,color='r', range=(-2,2))
plt.title("delta_2",fontsize=20)
plt.legend(["Est","True"])
#plt.hist(np.array(truepsi).reshape(-1),bins, alpha=0.5, color='r',range=(-30,30))
plt.show()
In [2322]:
plt.figure(figsize=(10,10))
plt.hist(diffab[:,2].reshape(-1),bins, alpha=0.5,color='b', range=(-30,30))
plt.hist(diffab[:,5].reshape(-1),bins, alpha=0.5,color='r', range=(-30,30))
plt.title("g2",fontsize=20)
plt.legend(["Est","True"])
#plt.hist(np.array(truepsi).reshape(-1),bins, alpha=0.5, color='r',range=(-30,30))
plt.show()
In [2323]:
diffab = np.concatenate([estab, trueab],1)
In [2324]:
print(path)
bhg = np.array(pd.read_csv(path +'/SSDF_Simu/MCMC/bhg.csv', header=None))
bm = bhg.mean(0)
tb = pd.read_csv(path + '/b.csv',index_col=0)
sims6/s6
In [2325]:
np.concatenate([np.round(bm.reshape(-1,2),2),np.round(np.array(tb),2)],1)
#print()
Out[2325]:
array([[ 1.  ,  0.01,  1.  ,  0.  ],
       [-0.17,  1.  , -0.19,  1.  ],
       [-0.02, -0.37, -0.01, -0.37],
       [-0.09, -0.17, -0.08, -0.16],
       [ 0.75,  0.39,  0.71,  0.37],
       [ 0.9 ,  0.3 ,  0.85,  0.28],
       [-0.35,  0.33, -0.35,  0.34],
       [-0.14,  0.84, -0.15,  0.83],
       [ 1.  ,  0.63,  0.95,  0.59],
       [-0.64, -0.63, -0.61, -0.6 ],
       [-0.72, -1.  , -0.67, -0.96],
       [ 1.01, -0.3 ,  0.99, -0.3 ]])
In [2326]:
beta
Out[2326]:
array([[ 0.2101511 , -0.26443244],
       [ 0.07913589, -0.37865738],
       [ 0.23324516, -0.28574408],
       [ 0.36199172, -0.28605789],
       [ 0.16117853, -0.26240796],
       [ 0.3507862 , -0.29320166],
       [ 0.33408456, -0.3291382 ],
       [ 0.60634148, -0.63687704],
       [ 0.05493777, -0.29652337],
       [ 0.32309326, -0.39057372],
       [ 0.37021015, -0.28765671],
       [ 0.42786682, -0.31823424],
       [ 0.16394416, -0.21049756],
       [ 0.28360878, -0.36986243],
       [ 0.19302925, -0.36035639],
       [ 0.11176297, -0.37248804],
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In [2327]:
tbeta
Out[2327]:
array([[ 0.20945292, -0.26355392],
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       [ 0.35379486, -0.20833665],
       [ 0.20878893, -0.23322678],
       [ 0.32627069, -0.15595552],
       [ 0.35645638, -0.20354692],
       [ 0.4412515 , -0.25200296],
       [ 0.203256  , -0.26175943],
       [ 0.46600116, -0.18549889],
       [ 0.32657934, -0.30959735],
       [ 0.3478234 , -0.31829639],
       [ 0.30153273, -0.42035325],
       [ 0.21512645, -0.3750932 ],
       [ 0.2733897 , -0.26355186],
       [ 0.25421682, -0.22248054],
       [ 0.20745531, -0.27527853],
       [ 0.20300977, -0.337085  ],
       [ 0.27311349, -0.23364395],
       [ 0.26564756, -0.23361782],
       [ 0.36458283, -0.17596768],
       [ 0.55309567, -0.38565581],
       [ 0.16821129, -0.43767165],
       [ 0.21951163, -0.35753285],
       [ 0.28216069, -0.34878188],
       [ 0.29632791, -0.39233913],
       [ 0.35062179, -0.16623561],
       [ 0.13895427, -0.39633935],
       [ 0.22775249, -0.40324267],
       [ 0.29753023, -0.19141923],
       [ 0.11129886, -0.3617582 ],
       [ 0.23391309, -0.32609389],
       [ 0.32877265, -0.3175716 ],
       [ 0.26370332, -0.2337178 ],
       [ 0.27398174, -0.39936161],
       [ 0.1925919 , -0.28075657],
       [ 0.26062124, -0.28865367],
       [ 0.15310454, -0.19089514],
       [ 0.4816893 , -0.34693523],
       [ 0.31509381, -0.42233008],
       [ 0.22718587, -0.20559001],
       [ 0.31925337, -0.30476122],
       [ 0.31024553, -0.21296256],
       [ 0.46123865, -0.22750755],
       [ 0.36571316, -0.42111194],
       [ 0.38338819, -0.2715517 ],
       [ 0.39250887, -0.49943601],
       [ 0.31159114, -0.29118925],
       [ 0.32848172, -0.32807805],
       [ 0.39851484, -0.28060229],
       [ 0.48963235, -0.28271061],
       [ 0.30547808, -0.48243194],
       [ 0.34915571, -0.28633241],
       [ 0.34486484, -0.29820984],
       [ 0.10892488, -0.31515988],
       [ 0.40924773, -0.11057326],
       [ 0.22611679, -0.16633972],
       [ 0.14838494, -0.37543898],
       [ 0.47562408, -0.3081772 ],
       [ 0.27060604, -0.19680211],
       [ 0.34727896, -0.15418339],
       [ 0.22851156, -0.28041025],
       [ 0.38777787, -0.28334759],
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       [ 0.36461446, -0.32337681],
       [ 0.42743515, -0.37324289],
       [ 0.24005892, -0.14422763],
       [ 0.33798457, -0.32327014],
       [ 0.21465939, -0.38707238],
       [ 0.21334295, -0.19764625],
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       [ 0.18554637, -0.31204378],
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       [ 0.36360285, -0.15083379],
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       [ 0.37777643, -0.27245632],
       [ 0.30998301, -0.35716354],
       [ 0.11646876, -0.18399346],
       [ 0.34042326, -0.30034857],
       [ 0.11614586, -0.37821472],
       [ 0.32123193, -0.33692617],
       [ 0.33165452, -0.26753606],
       [ 0.37961299, -0.22955992],
       [ 0.18218277, -0.326713  ],
       [ 0.30664128, -0.45567847],
       [ 0.27071755, -0.2842985 ],
       [ 0.1398856 , -0.2556068 ],
       [ 0.1630934 , -0.40822708],
       [ 0.2746628 , -0.43165061],
       [ 0.36042026, -0.38774528],
       [ 0.27447475, -0.31171129],
       [ 0.32635405, -0.27163377],
       [ 0.20078501, -0.25997566],
       [ 0.32433265, -0.38762278],
       [ 0.42302687, -0.3030225 ],
       [ 0.20950549, -0.25836111],
       [ 0.25452551, -0.4406527 ],
       [ 0.09856039, -0.29998775],
       [ 0.09280572, -0.04475381],
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       [ 0.12860123, -0.20088225],
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       [ 0.31006673, -0.33255308],
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       [ 0.25347295, -0.08893168],
       [ 0.2904667 , -0.26345065],
       [ 0.4369668 , -0.3390645 ],
       [ 0.23830712, -0.27553089],
       [ 0.26703247, -0.20814291],
       [ 0.18368504, -0.26729333],
       [ 0.35060547, -0.21283184],
       [ 0.50819984, -0.41161123],
       [ 0.36274141, -0.23764476],
       [ 0.16990334, -0.40723213],
       [ 0.32904963, -0.32714232],
       [ 0.21208477, -0.27579289],
       [ 0.46665525, -0.22422002],
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       [ 0.29686046, -0.2413888 ],
       [ 0.22880209, -0.57636931],
       [ 0.10333832, -0.39062919],
       [ 0.22944443, -0.09168703],
       [ 0.34711143, -0.1303578 ],
       [ 0.32527702, -0.27647825],
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       [ 0.27636295, -0.2962555 ],
       [ 0.35831769, -0.35727731],
       [ 0.3760326 , -0.25636831],
       [ 0.31344037, -0.40394525],
       [ 0.34299711, -0.16481043],
       [ 0.22706223, -0.32106499],
       [ 0.23705873, -0.33329439],
       [ 0.30418821, -0.41697123],
       [ 0.17335438, -0.30753538],
       [ 0.3696524 , -0.27554695],
       [ 0.39916805, -0.18688313],
       [ 0.38761265, -0.49337614],
       [ 0.36115896, -0.3781145 ],
       [ 0.25989541, -0.54256384],
       [ 0.23975758, -0.52798157],
       [ 0.25875971, -0.20658585],
       [ 0.3383147 , -0.4970021 ],
       [ 0.33229969, -0.28228128],
       [ 0.43047852, -0.2709259 ],
       [ 0.33065782, -0.23140986],
       [ 0.24605464, -0.41098278],
       [ 0.30505637, -0.30139307],
       [ 0.27989229, -0.31759756],
       [ 0.39357307, -0.36960651],
       [ 0.09665011, -0.19502709],
       [ 0.47540589, -0.30248311],
       [ 0.2278445 , -0.18399036],
       [ 0.4045711 , -0.17256261],
       [ 0.41992927, -0.16178005],
       [ 0.33735161, -0.47720359],
       [ 0.27363819, -0.2622279 ],
       [ 0.40249352, -0.4773694 ],
       [ 0.35113145, -0.14704374],
       [ 0.21639764, -0.4118187 ],
       [ 0.35682633, -0.24019995],
       [ 0.31527587, -0.29395236],
       [ 0.40689558, -0.2841192 ],
       [ 0.34471899, -0.01849042],
       [ 0.04709215, -0.40897525],
       [ 0.27676173, -0.43077797],
       [ 0.22203679, -0.25691063],
       [ 0.35526542, -0.39333128],
       [ 0.3351578 , -0.19650023],
       [ 0.39470234, -0.33945099],
       [ 0.30692761, -0.07700955],
       [ 0.41929445, -0.20770255],
       [ 0.43281568, -0.30994561],
       [ 0.16292815, -0.24880765],
       [ 0.35196407, -0.20393395],
       [ 0.33163828, -0.29221323],
       [ 0.45629321, -0.33599312],
       [ 0.19124607, -0.26558276],
       [ 0.32419576, -0.26845402],
       [ 0.23947667, -0.38656615],
       [ 0.30305915, -0.4209182 ],
       [ 0.3604816 , -0.28484454],
       [ 0.3072549 , -0.40517   ],
       [ 0.20949451, -0.3344544 ],
       [ 0.44434177, -0.43058747],
       [ 0.30496128, -0.34750729],
       [ 0.26238389, -0.41812656],
       [ 0.34514809, -0.40587118],
       [ 0.1623013 , -0.39859091],
       [ 0.23745596, -0.3985582 ],
       [ 0.25145571, -0.26279558],
       [ 0.2646895 , -0.33990767],
       [ 0.40639916, -0.1798856 ],
       [ 0.09175035, -0.30381591],
       [ 0.36636119, -0.2308751 ],
       [ 0.12540518, -0.31304098],
       [ 0.11869957, -0.16652414],
       [ 0.15918183, -0.44802006],
       [ 0.35275296, -0.26409959],
       [ 0.40872891, -0.32887962],
       [ 0.19721248, -0.30180549],
       [ 0.43584557, -0.1611527 ],
       [ 0.27801806, -0.31663018],
       [ 0.2870554 , -0.23878235],
       [ 0.1961982 , -0.26953564],
       [ 0.21949349, -0.3677063 ],
       [ 0.26633169, -0.16917593],
       [ 0.35162586, -0.19718644],
       [ 0.26471353, -0.14257894],
       [ 0.20722007, -0.27149045],
       [ 0.14588305, -0.21097165],
       [ 0.18164369, -0.36697923],
       [ 0.41065722, -0.5032364 ],
       [ 0.35325875, -0.40050239],
       [ 0.31904092, -0.31747511],
       [ 0.40911026, -0.39813358],
       [ 0.18328969, -0.29571209],
       [ 0.28042215, -0.30461332],
       [ 0.28289559, -0.2360514 ],
       [ 0.20450919, -0.40023505],
       [ 0.3237023 , -0.53898028],
       [ 0.48291547, -0.28001047],
       [ 0.15264622, -0.15578441],
       [ 0.27090656, -0.26226725],
       [ 0.11398295, -0.1273296 ],
       [ 0.53718582, -0.3738699 ],
       [ 0.36727393, -0.19513437],
       [ 0.21066334, -0.30478287],
       [ 0.36470266, -0.31413409],
       [ 0.36093709, -0.16323507],
       [ 0.27199631, -0.27165076],
       [ 0.18918544, -0.272977  ],
       [ 0.28873148, -0.41650857],
       [ 0.4753984 , -0.25595644],
       [ 0.22584605, -0.07419753],
       [ 0.05595331, -0.26000454],
       [ 0.45440034, -0.50467403],
       [ 0.3047758 , -0.28005407],
       [ 0.27670854, -0.42666866],
       [ 0.22034161, -0.28976822],
       [ 0.42634828, -0.19199947],
       [ 0.364554  , -0.32773735],
       [ 0.49202517, -0.334225  ],
       [ 0.36236462, -0.26229847],
       [ 0.25385792, -0.43290216],
       [ 0.20557523, -0.2776327 ],
       [ 0.30276738, -0.38453835],
       [ 0.22834899, -0.41996077],
       [ 0.34861169, -0.449235  ],
       [ 0.40523413, -0.41111485],
       [ 0.31054905, -0.39662186],
       [ 0.25138951, -0.28738054],
       [ 0.28979919, -0.40373453],
       [ 0.42858299, -0.14894327],
       [ 0.44221437, -0.49040212],
       [ 0.43476968, -0.23641734],
       [ 0.32618387, -0.30353784],
       [ 0.21767824, -0.33250766],
       [ 0.31968107, -0.20400731],
       [ 0.40951951, -0.30597525],
       [ 0.33951047, -0.51525963],
       [ 0.37874226, -0.27065767],
       [ 0.37904161, -0.38221771],
       [ 0.33287678, -0.34486348],
       [ 0.29890886, -0.25158467],
       [ 0.30913943, -0.32853875],
       [ 0.22014029, -0.12179711],
       [ 0.24640002, -0.08122833],
       [ 0.24871056, -0.3700695 ],
       [ 0.18488772, -0.32846886],
       [ 0.26240762, -0.23774633],
       [ 0.20830433, -0.26602162],
       [ 0.32756098, -0.35230871],
       [ 0.16266989, -0.09850469],
       [ 0.18209263, -0.43692042],
       [ 0.47104517, -0.40593997],
       [ 0.41953749, -0.38938312],
       [ 0.36174173, -0.42654731],
       [ 0.17951503, -0.42537443],
       [ 0.33490572, -0.40600843],
       [ 0.45193297, -0.1662298 ],
       [ 0.29269268, -0.30025711],
       [ 0.40966692, -0.26502415],
       [ 0.28353029, -0.29917803],
       [ 0.34599431, -0.48093971],
       [ 0.4145112 , -0.48561992],
       [ 0.33989984, -0.35336752],
       [ 0.47327704, -0.13840516],
       [ 0.36407579, -0.27020681],
       [ 0.39334492, -0.25181644],
       [ 0.33303601, -0.36913365],
       [ 0.15046729, -0.18116147],
       [ 0.15562851, -0.21674734],
       [ 0.40866707, -0.32139868],
       [ 0.44884605, -0.20141626],
       [ 0.38516942, -0.33474149],
       [ 0.3872935 , -0.35903497],
       [ 0.38266633, -0.0455624 ],
       [ 0.05400226, -0.14938213],
       [ 0.36339664, -0.20985378],
       [ 0.32891883, -0.47231687],
       [ 0.35552094, -0.31011134],
       [ 0.18407154, -0.32960938],
       [ 0.29507156, -0.36029331],
       [ 0.14355173, -0.29185941],
       [ 0.41563988, -0.28074115],
       [ 0.28915313, -0.35705617],
       [ 0.35003035, -0.39050222],
       [ 0.21036023, -0.30535536],
       [ 0.15122465, -0.20531746],
       [ 0.09644504, -0.41551759],
       [ 0.45361031, -0.31101917],
       [ 0.0807841 , -0.1761839 ],
       [ 0.16185269, -0.21865064],
       [ 0.3358058 , -0.41399486],
       [ 0.11895594, -0.42265398],
       [ 0.31621897, -0.26991804],
       [ 0.16903561, -0.30770779],
       [ 0.06653243, -0.49704607],
       [ 0.24954335, -0.1868321 ],
       [ 0.33635109, -0.11802093],
       [ 0.24719319, -0.45165298],
       [ 0.3725365 , -0.46328697]])
In [448]:
#pd.DataFrame(np.round(diffab[:40],2))


diffab[:,1].reshape(1,-1).dot(diffab[:,1].reshape(-1,1))
Out[448]:
array([[43149.31865021]])
In [450]:
diffab[:,4].reshape(1,-1).dot(diffab[:,4].reshape(-1,1))
Out[450]:
array([[39126.6889543]])
In [451]:
diffab[:,0].reshape(1,-1).dot(diffab[:,0].reshape(-1,1))
Out[451]:
array([[67646.2429252]])
In [453]:
diffab[:,3].reshape(1,-1).dot(diffab[:,3].reshape(-1,1))
Out[453]:
array([[65036.33133058]])
In [764]:
### dat = np.load('sims6/s1' + '/SSDF_Simu.npz')
import re
def ab5(folder,name):
    #dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = ghg
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = bhg
    deltag = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/phig.csv', header=None))
    L = ghg.shape[1]
    print(L)
    print(a.shape)
    
    mugamg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/gammu.csv', header=None))
    mupsig = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/psimu.csv', header=None))
    try:
        delta = np.array(pd.read_csv(folder + '/delta_bar.csv',header=None, delimiter=' ')).reshape(-1)
    except:
        delta = [1, -1]
    #print(b)
    
    #a2 = np.median(ghg,0)#.median(0)
     
    #b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    
    #ta = pd.read_csv(folder + '/a.csv',index_col=0)
    #tb = pd.read_csv(folder + '/b.csv',index_col=0)
    #mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    #mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    
    ta = pd.read_csv(folder + '/a.csv',index_col=0)
    tb = pd.read_csv(folder + '/b.csv',index_col=0)
    mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    
    #plt.plot(ap)
    plt.figure(figsize=(20,10))
    plt.subplot(3,4,1)
    for i in range(L):
        plt.plot(a[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(ta)[i],ls='--', color= cols[i])
    plt.title('A_LOADING', fontsize=15)
    #plt.show()

    plt.subplot(3,4,2)
    for i in range(2):
        plt.plot(deltag[:,i],color=cols[i], alpha=0.5)
        plt.axhline(delta[i],ls='--', color= cols[i] )
    plt.title('Delta', fontsize=15)
    #plt.show()

    plt.subplot(3,4,5)
    #plt.plot(bp1)
    for i in range(L-1):
        plt.plot(b[:,i*2],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    plt.title('B_LOADING_1', fontsize=15)
    #plt.show()

    plt.subplot(3,4,6)
    #plt.plot(bp2)
    for i in range(L-1):
        plt.plot(b[:, i*2+1],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,1]),ls='--', color=cols[i])
    plt.title('B_LOADING_2', fontsize=15)    
    
    plt.subplot(3,4,9)
    for i in range(L):
        plt.plot(mugamg[:, i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mugam)[i],ls='--', color=cols[i])
    plt.title('a_h', fontsize=15)    

    plt.subplot(3,4,10)
    for i in range(L-1):
        plt.plot(mupsig[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mupsi)[i],ls='--', color=cols[i])
    plt.title('b_h', fontsize=15)    
    

    tmp = int( re.findall('\/s(\d)', folder)[0])
    folder = 'sims6/s%i' %(tmp+1)
    
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = ghg
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = bhg
    deltag = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/phig.csv', header=None))
    L = ghg.shape[1]

    
    mugamg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/gammu.csv', header=None))
    mupsig = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/psimu.csv', header=None))
    try:
        delta = np.array(pd.read_csv(folder + '/delta_bar.csv',header=None, delimiter=' ')).reshape(-1)
    except:
        delta = [1, -1]
    #print(b)
    
    #a2 = np.median(ghg,0)#.median(0)
     
    #b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    
    #ta = pd.read_csv(folder + '/a.csv',index_col=0)
    #tb = pd.read_csv(folder + '/b.csv',index_col=0)
    #mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    #mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    

    
    ta = pd.read_csv(folder + '/a.csv',index_col=0)
    tb = pd.read_csv(folder + '/b.csv',index_col=0)
    mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    
    #plt.plot(ap)
    plt.subplot(3,4,3)
    for i in range(L):
        plt.plot(a[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(ta)[i],ls='--', color= cols[i])
    plt.title('A_LOADING', fontsize=15)
    #plt.show()

    plt.subplot(3,4,4)
    for i in range(2):
        plt.plot(deltag[:,i],color=cols[i], alpha=0.5)
        plt.axhline(delta[i],ls='--', color= cols[i] )
    plt.title('Delta', fontsize=15)
    #plt.show()

    plt.subplot(3,4,7)
    #plt.plot(bp1)
    for i in range(L-1):
        plt.plot(b[:,i*2],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    plt.title('B_LOADING_1', fontsize=15)
    #plt.show()

    plt.subplot(3,4,8)
    #plt.plot(bp2)
    for i in range(L-1):
        plt.plot(b[:, i*2+1],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i,1]),ls='--', color=cols[i])
    plt.title('B_LOADING_2', fontsize=15)    
    
    plt.subplot(3,4,11)
    for i in range(L):
        plt.plot(mugamg[:, i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mugam)[i],ls='--', color=cols[i])
    plt.title('a_h', fontsize=15)    

    plt.subplot(3,4,12)
    for i in range(L-1):
        plt.plot(mupsig[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mupsi)[i],ls='--', color=cols[i])
    plt.title('b_h', fontsize=15)    
    
    plt.suptitle(name, fontsize=20)
    plt.savefig('Simu_Dynamic_S%i.png' %tmp)
    
    
    
    plt.show()
    
    

ab5('sims6/s3','Beta Fix                VS                 Beta No Fix')
12
(65133, 12)
In [788]:
ab5('sims6/s5','Beta No Fix                VS                 Beta Fix')
12
(68100, 12)
In [1325]:
# Only 1 dimension for B_Loading

def ab6(folder):
    #dat = np.load(folder + '/SSDF_Simu.npz')
    #a = dat['ghg'].mean(0)
    ghg =np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/ghg.csv', header=None))
    a = ghg
    bhg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/bhg.csv', header=None))
    b = bhg
    deltag = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/phig.csv', header=None))
    L = ghg.shape[1]
    print(L)
    print(a.shape)
    
    mugamg = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/gammu.csv', header=None))
    mupsig = np.array(pd.read_csv(folder +'/SSDF_Simu/MCMC/psimu.csv', header=None))
    delta = [0.5, -0.5]
    #print(b)
    
    #a2 = np.median(ghg,0)#.median(0)
     
    #b2 = np.median(bhg,0).reshape(-1,2)[:-1, :]
    
    
    #ta = pd.read_csv(folder + '/a.csv',index_col=0)
    #tb = pd.read_csv(folder + '/b.csv',index_col=0)
    #mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    #mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    
    ta = pd.read_csv(folder + '/a.csv',index_col=0)
    tb = pd.read_csv(folder + '/b.csv',index_col=0)
    mugam = pd.read_csv(folder + '/mugam.csv',index_col=0)
    mupsi = pd.read_csv(folder + '/mupsi.csv',index_col=0)
    
    #plt.plot(ap)
    plt.figure(figsize=(15,15))
    plt.subplot(3,2,1)
    for i in range(L):
        plt.plot(a[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(ta)[i],ls='--', color= cols[i])
    plt.title('A_LOADING', fontsize=15)
    #plt.show()

    plt.subplot(3,2,2)
    for i in range(1):
        plt.plot(deltag[:,i],color=cols[i], alpha=0.5)
        plt.axhline(delta[i],ls='--', color= cols[i] )
    plt.title('Delta', fontsize=15)
    #plt.show()

    plt.subplot(3,2,3)
    #plt.plot(bp1)
    for i in range(L-1):
        plt.plot(b[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(np.array(tb)[i]),ls='--', color= cols[i])
    plt.title('B_LOADING_1', fontsize=15)
    #plt.show()

    #plt.subplot(3,2,4)

    
    plt.subplot(3,2,5)
    for i in range(L):
        plt.plot(mugamg[:, i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mugam)[i],ls='--', color=cols[i])
    plt.title('a_h', fontsize=15)    

    plt.subplot(3,2,6)
    for i in range(L-1):
        plt.plot(mupsig[:,i],color=cols[i], alpha=0.5)
        plt.axhline(np.array(mupsi)[i],ls='--', color=cols[i])
    plt.title('b_h', fontsize=15)    
    
    plt.savefig('S1_B1.png')
    plt.show()
    
    #plt.title('B_LOADING_1', fontsize=15)
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.boxplot(a[-10000:,i])
        plt.scatter(1, np.array(ta)[i], color='r', marker='x', s = 60 )
        #plt.axvline(np.array(np.array(ta)[i]),ls='--', color= cols[i])
    #plt.savefig('Simu_S3_100_A_Loading.png')
    plt.show()
    
    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.boxplot(mupsig[-10000:,i])
        plt.scatter(1, np.array(mupsi)[i], color='r', marker='x', s = 60 )
        #plt.axvline(np.array(np.array(tb)[i,0]),ls='--', color= cols[i])
    plt.suptitle('b_h', fontsize=15)
    plt.tight_layout()
    #plt.savefig('Simu_S2_E5_b_h.png')
    plt.show()

    for i in range(L-1):
        plt.subplot(4,4,i+1)
        plt.boxplot(mugamg[-10000:,i])
        plt.scatter(1, np.array(mugam)[i], color='r', marker='x', s = 60 )
        #plt.axvline(np.array(tb)[i,1],ls='--', color= cols[i])
    plt.suptitle('a_h', fontsize=15)
    plt.tight_layout()
    #plt.savefig('Simu_S2_E5_a_h.png')
    plt.show()
    #return a,b, deltag, mugamg, mupsig#, a2, b2
    
    

ab6('sims8/s1')
10
(200000, 10)
In [800]:
from scipy.linalg import cholesky 

test = np.array([[1, 0.5], [0.5, 2]])
display(test)
cholesky(test,True)
array([[1. , 0.5],
       [0.5, 2. ]])
Out[800]:
array([[1.        , 0.        ],
       [0.5       , 1.32287566]])
In [661]:
from Utils.Utils import *
In [788]:
test2 =test[np.tril_indices(test.shape[0], 0)]
In [789]:
test2
Out[789]:
array([1. , 0.5, 2. ])
In [790]:
test3 = xpnd(test2)
test3
Out[790]:
array([[1. , 0.5],
       [0.5, 2. ]])
In [791]:
chol(test3)
Out[791]:
array([[1.        , 0.        ],
       [0.5       , 1.32287566]])
In [829]:
t1 = []
t2 = []

for i in range(10000):
    t1.append(mvn(test))
    t2.append(cholesky(test,True).dot(np.random.standard_normal(2) ))
In [825]:
np.random.standard_normal(5)
Out[825]:
array([ 0.08644535, -0.15805528,  0.33296943, -0.52109369,  0.48727846])
In [818]:
t2
Out[818]:
[array([[2.83587873, 0.        ],
        [1.41793937, 3.75151493]]), array([[1.06040318, 0.        ],
        [0.53020159, 1.40278155]]), array([[1.81535844, 0.        ],
        [0.90767922, 2.40149349]]), array([[1.13198082, 0.        ],
        [0.56599041, 1.49746987]]), array([[1.29418848, 0.        ],
        [0.64709424, 1.71205044]]), array([[1.45227847, 0.        ],
        [0.72613924, 1.92118383]]), array([[2.39904553, 0.        ],
        [1.19952277, 3.17363893]]), array([[2.85422928, 0.        ],
        [1.42711464, 3.77579042]]), array([[1.57056422, 0.        ],
        [0.78528211, 2.07766117]]), array([[0.81711602, 0.        ],
        [0.40855801, 1.0809429 ]]), array([[1.07338359, 0.        ],
        [0.53669179, 1.41995301]]), array([[3.26315195, 0.        ],
        [1.63157597, 4.31674427]]), array([[1.36436632, 0.        ],
        [0.68218316, 1.80488699]]), array([[1.58239385, 0.        ],
        [0.79119692, 2.0933103 ]]), array([[1.07090017, 0.        ],
        [0.53545008, 1.41666776]]), array([[2.49136676, 0.        ],
        [1.24568338, 3.29576843]]), array([[3.53806706, 0.        ],
        [1.76903353, 4.68042278]]), array([[0.62064262, 0.        ],
        [0.31032131, 0.82103301]]), array([[2.0633155 , 0.        ],
        [1.03165775, 2.72950984]]), array([[3.47685137, 0.        ],
        [1.73842568, 4.59944203]]), array([[0.46882909, 0.        ],
        [0.23441455, 0.62020259]]), array([[3.05204014, 0.        ],
        [1.52602007, 4.0374696 ]]), array([[2.59268725, 0.        ],
        [1.29634363, 3.42980285]]), array([[2.51230336, 0.        ],
        [1.25615168, 3.32346495]]), array([[1.54695232, 0.        ],
        [0.77347616, 2.04642556]]), array([[2.7068528 , 0.        ],
        [1.3534264 , 3.58082967]]), array([[1.39417692, 0.        ],
        [0.69708846, 1.84432271]]), array([[2.05252463, 0.        ],
        [1.02626232, 2.71523487]]), array([[1.57103467, 0.        ],
        [0.78551734, 2.07828352]]), array([[3.21134239, 0.        ],
        [1.60567119, 4.24820666]]), array([[0.98277479, 0.        ],
        [0.4913874 , 1.30008885]]), array([[2.82523791, 0.        ],
        [1.41261896, 3.73743846]]), array([[3.0275504 , 0.        ],
        [1.5137752 , 4.00507272]]), array([[1.14540717, 0.        ],
        [0.57270358, 1.51523125]]), array([[4.02190009, 0.        ],
        [2.01095004, 5.32047371]]), array([[0.62917592, 0.        ],
        [0.31458796, 0.83232151]]), array([[1.38318993, 0.        ],
        [0.69159497, 1.82978829]]), array([[0.7860855 , 0.        ],
        [0.39304275, 1.03989338]]), array([[1.24395527, 0.        ],
        [0.62197764, 1.64559814]]), array([[2.16773744, 0.        ],
        [1.08386872, 2.86764709]]), array([[2.38067422, 0.        ],
        [1.19033711, 3.14933597]]), array([[4.82678831, 0.        ],
        [2.41339416, 6.38524075]]), array([[2.27834904, 0.        ],
        [1.13917452, 3.01397248]]), array([[0.95250931, 0.        ],
        [0.47625465, 1.26005138]]), array([[2.2787774 , 0.        ],
        [1.1393887 , 3.01453914]]), array([[3.13303062, 0.        ],
        [1.56651531, 4.14460994]]), array([[0.89779862, 0.        ],
        [0.44889931, 1.18767594]]), array([[2.17481965, 0.        ],
        [1.08740983, 2.87701598]]), array([[5.14504984, 0.        ],
        [2.57252492, 6.80626118]]), array([[0.61350249, 0.        ],
        [0.30675124, 0.81158751]]), array([[2.99563391, 0.        ],
        [1.49781695, 3.96285117]]), array([[1.80758931, 0.        ],
        [0.90379465, 2.39121589]]), array([[1.98525431, 0.        ],
        [0.99262716, 2.6262446 ]]), array([[1.48629889, 0.        ],
        [0.74314945, 1.96618862]]), array([[2.25132513, 0.        ],
        [1.12566256, 2.97822321]]), array([[0.00590561, 0.        ],
        [0.0029528 , 0.00781238]]), array([[2.68548446, 0.        ],
        [1.34274223, 3.55256202]]), array([[4.73117421, 0.        ],
        [2.36558711, 6.25875519]]), array([[2.5734911 , 0.        ],
        [1.28674555, 3.40440873]]), array([[3.32810927, 0.        ],
        [1.66405463, 4.40267473]]), array([[3.19486253, 0.        ],
        [1.59743126, 4.22640586]]), array([[1.77739244, 0.        ],
        [0.88869622, 2.35126919]]), array([[1.17347636, 0.        ],
        [0.58673818, 1.55236331]]), array([[1.59266382, 0.        ],
        [0.79633191, 2.10689619]]), array([[4.6249333 , 0.        ],
        [2.31246665, 6.11821168]]), array([[1.62845052, 0.        ],
        [0.81422526, 2.15423756]]), array([[0.71970317, 0.        ],
        [0.35985159, 0.95207781]]), array([[1.59035127, 0.        ],
        [0.79517563, 2.10383697]]), array([[1.59069458, 0.        ],
        [0.79534729, 2.10429114]]), array([[1.5440327 , 0.        ],
        [0.77201635, 2.04256327]]), array([[3.31956939, 0.        ],
        [1.65978469, 4.39137753]]), array([[1.62227533, 0.        ],
        [0.81113767, 2.14606855]]), array([[2.42009146, 0.        ],
        [1.21004573, 3.20148007]]), array([[3.31470226, 0.        ],
        [1.65735113, 4.38493893]]), array([[1.87090747, 0.        ],
        [0.93545374, 2.47497795]]), array([[1.49973612, 0.        ],
        [0.74986806, 1.9839644 ]]), array([[2.46645359, 0.        ],
        [1.23322679, 3.26281141]]), array([[2.1889176 , 0.        ],
        [1.0944588 , 2.89566581]]), array([[2.6874904 , 0.        ],
        [1.3437452 , 3.55521562]]), array([[1.20740922, 0.        ],
        [0.60370461, 1.59725227]]), array([[0.85379034, 0.        ],
        [0.42689517, 1.12945846]]), array([[2.42150308, 0.        ],
        [1.21075154, 3.20334747]]), array([[1.1822148 , 0.        ],
        [0.5911074 , 1.56392318]]), array([[0.30269014, 0.        ],
        [0.15134507, 0.40042142]]), array([[1.86704697, 0.        ],
        [0.93352349, 2.46987099]]), array([[2.60219167, 0.        ],
        [1.30109584, 3.44237602]]), array([[2.39437405, 0.        ],
        [1.19718702, 3.16745914]]), array([[3.19997089, 0.        ],
        [1.59998545, 4.23316359]]), array([[1.67797259, 0.        ],
        [0.83898629, 2.21974909]]), array([[1.14546524, 0.        ],
        [0.57273262, 1.51530808]]), array([[1.53890944, 0.        ],
        [0.76945472, 2.03578584]]), array([[2.15551667, 0.        ],
        [1.07775834, 2.85148053]]), array([[3.14545776, 0.        ],
        [1.57272888, 4.1610495 ]]), array([[2.57252206, 0.        ],
        [1.28626103, 3.4031268 ]]), array([[3.34887359, 0.        ],
        [1.6744368 , 4.43014335]]), array([[-0.46527661,  0.        ],
        [-0.2326383 , -0.61550309]]), array([[2.35427101, 0.        ],
        [1.1771355 , 3.1144078 ]]), array([[2.60644708, 0.        ],
        [1.30322354, 3.44800539]]), array([[1.90079365, 0.        ],
        [0.95039683, 2.51451365]]), array([[1.22802574, 0.        ],
        [0.61401287, 1.62452536]]), array([[2.16182741, 0.        ],
        [1.0809137 , 2.85982885]]), array([[0.48436233, 0.        ],
        [0.24218116, 0.64075113]]), array([[1.44225497, 0.        ],
        [0.72112748, 1.90792398]]), array([[2.56658456, 0.        ],
        [1.28329228, 3.39527223]]), array([[1.68522996, 0.        ],
        [0.84261498, 2.22934969]]), array([[1.94610895, 0.        ],
        [0.97305448, 2.57446016]]), array([[2.43754089, 0.        ],
        [1.21877044, 3.2245635 ]]), array([[1.81386974, 0.        ],
        [0.90693487, 2.39952412]]), array([[2.31686824, 0.        ],
        [1.15843412, 3.06492859]]), array([[2.91599075, 0.        ],
        [1.45799538, 3.85749318]]), array([[1.88657807, 0.        ],
        [0.94328903, 2.49570819]]), array([[1.53800417, 0.        ],
        [0.76900209, 2.03458828]]), array([[1.81978112, 0.        ],
        [0.90989056, 2.40734414]]), array([[2.09385787, 0.        ],
        [1.04692893, 2.7699136 ]]), array([[0.93194786, 0.        ],
        [0.46597393, 1.23285113]]), array([[1.28068235, 0.        ],
        [0.64034117, 1.6941835 ]]), array([[3.22792985, 0.        ],
        [1.61396493, 4.27014982]]), array([[0.73894387, 0.        ],
        [0.36947194, 0.97753086]]), array([[2.30070052, 0.        ],
        [1.15035026, 3.04354071]]), array([[1.91709011, 0.        ],
        [0.95854506, 2.53607184]]), array([[2.8269651 , 0.        ],
        [1.41348255, 3.7397233 ]]), array([[2.42351767, 0.        ],
        [1.21175883, 3.20601252]]), array([[2.02044793, 0.        ],
        [1.01022397, 2.67280139]]), array([[0.59724166, 0.        ],
        [0.29862083, 0.79007645]]), array([[1.03088958, 0.        ],
        [0.51544479, 1.36373873]]), array([[2.67482311, 0.        ],
        [1.33741156, 3.53845838]]), array([[2.20725911, 0.        ],
        [1.10362955, 2.91992934]]), array([[2.3288053 , 0.        ],
        [1.16440265, 3.08071984]]), array([[2.04557394, 0.        ],
        [1.02278697, 2.70603997]]), array([[3.5161155 , 0.        ],
        [1.75805775, 4.65138359]]), array([[2.21717306, 0.        ],
        [1.10858653, 2.93304427]]), array([[2.035653  , 0.        ],
        [1.0178265 , 2.69291579]]), array([[1.19916511, 0.        ],
        [0.59958255, 1.58634633]]), array([[1.85795567, 0.        ],
        [0.92897784, 2.45784433]]), array([[2.53697091, 0.        ],
        [1.26848546, 3.35609706]]), array([[2.86343873, 0.        ],
        [1.43171936, 3.78797338]]), array([[2.49726536, 0.        ],
        [1.24863268, 3.30357155]]), array([[1.21273405, 0.        ],
        [0.60636703, 1.60429636]]), array([[1.90813116, 0.        ],
        [0.95406558, 2.52422025]]), array([[2.12056967, 0.        ],
        [1.06028483, 2.80524999]]), array([[0.19452653, 0.        ],
        [0.09726327, 0.25733442]]), array([[2.38630858, 0.        ],
        [1.19315429, 3.15678953]]), array([[3.12357184, 0.        ],
        [1.56178592, 4.13209715]]), array([[3.27355669, 0.        ],
        [1.63677835, 4.33050845]]), array([[2.2918392 , 0.        ],
        [1.1459196 , 3.03181828]]), array([[0.8908766 , 0.        ],
        [0.4454383 , 1.17851897]]), array([[1.59362538, 0.        ],
        [0.79681269, 2.10816822]]), array([[0.27887225, 0.        ],
        [0.13943612, 0.36891331]]), array([[0.87734196, 0.        ],
        [0.43867098, 1.16061432]]), array([[2.76541298, 0.        ],
        [1.38270649, 3.65829751]]), array([[0.67755309, 0.        ],
        [0.33877655, 0.89631849]]), array([[2.77045197, 0.        ],
        [1.38522598, 3.66496346]]), array([[1.36308227, 0.        ],
        [0.68154114, 1.80318835]]), array([[0.86286529, 0.        ],
        [0.43143265, 1.14146349]]), array([[0.02098734, 0.        ],
        [0.01049367, 0.02776364]]), array([[2.93680109, 0.        ],
        [1.46840055, 3.88502267]]), array([[3.12928799, 0.        ],
        [1.56464399, 4.1396589 ]]), array([[1.66740798, 0.        ],
        [0.83370399, 2.20577343]]), array([[2.31087691, 0.        ],
        [1.15543846, 3.05700281]]), array([[4.22144308, 0.        ],
        [2.11072154, 5.58444428]]), array([[0.6901407 , 0.        ],
        [0.34507035, 0.91297034]]), array([[2.549811  , 0.        ],
        [1.2749055 , 3.37308289]]), array([[2.36264254, 0.        ],
        [1.18132127, 3.12548229]]), array([[2.03948112, 0.        ],
        [1.01974056, 2.69797992]]), array([[1.27132786, 0.        ],
        [0.63566393, 1.68180868]]), array([[2.39943042, 0.        ],
        [1.19971521, 3.17414809]]), array([[3.6933035 , 0.        ],
        [1.84665175, 4.88578129]]), array([[2.63623954, 0.        ],
        [1.31811977, 3.48741711]]), array([[2.18105185, 0.        ],
        [1.09052593, 2.8852604 ]]), array([[0.74704712, 0.        ],
        [0.37352356, 0.98825045]]), array([[2.67008652, 0.        ],
        [1.33504326, 3.53219246]]), array([[0.82152454, 0.        ],
        [0.41076227, 1.08677481]]), array([[3.08830194, 0.        ],
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        [1.34059042, 3.54686887]]), array([[2.15776143, 0.        ],
        [1.07888071, 2.85445006]]), array([[2.38796765, 0.        ],
        [1.19398382, 3.15898427]]), array([[3.75062551, 0.        ],
        [1.87531276, 4.96161118]]), array([[-0.53824324,  0.        ],
        [-0.26912162, -0.71202887]]), array([[2.77910879, 0.        ],
        [1.38955439, 3.67641536]]), array([[2.7921909 , 0.        ],
        [1.39609545, 3.69372136]]), array([[1.66062829, 0.        ],
        [0.83031415, 2.19680474]]), array([[1.78696892, 0.        ],
        [0.89348446, 2.36393768]]), array([[0.65098635, 0.        ],
        [0.32549318, 0.861174  ]]), array([[2.15709173, 0.        ],
        [1.07854587, 2.85356414]]), array([[1.48188325, 0.        ],
        [0.74094163, 1.96034728]]), array([[0.81471661, 0.        ],
        [0.40735831, 1.07776877]]), array([[2.02778711, 0.        ],
        [1.01389356, 2.68251021]]), array([[2.6988517 , 0.        ],
        [1.34942585, 3.57024521]]), array([[1.31943537, 0.        ],
        [0.65971769, 1.74544893]]), array([[3.33530897, 0.        ],
        [1.66765448, 4.41219904]]), array([[2.42803038, 0.        ],
        [1.21401519, 3.21198228]]), array([[2.55334415, 0.        ],
        [1.27667207, 3.37775681]]), array([[1.63502569, 0.        ],
        [0.81751284, 2.16293568]]), array([[2.90929434, 0.        ],
        [1.45464717, 3.84863465]]), array([[2.61464556, 0.        ],
        [1.30732278, 3.45885096]]), array([[1.36232065, 0.        ],
        [0.68116032, 1.80218082]]), array([[2.20905206, 0.        ],
        [1.10452603, 2.92230119]]), array([[1.79230316, 0.        ],
        [0.89615158, 2.37099422]]), array([[4.01987029, 0.        ],
        [2.00993515, 5.31778855]]), array([[1.09930098, 0.        ],
        [0.54965049, 1.45423851]]), array([[0.7945771 , 0.        ],
        [0.39728855, 1.0511267 ]]), array([[1.53269002, 0.        ],
        [0.76634501, 2.02755832]]), array([[1.34036833, 0.        ],
        [0.67018417, 1.77314063]]), array([[1.40198769, 0.        ],
        [0.70099384, 1.85465538]]), array([[1.75772918, 0.        ],
        [0.87886459, 2.32525715]]), array([[1.91268066, 0.        ],
        [0.95634033, 2.53023869]]), array([[3.89444358, 0.        ],
        [1.94722179, 5.15186461]]), array([[1.73526428, 0.        ],
        [0.86763214, 2.29553887]]), array([[1.9010295 , 0.        ],
        [0.95051475, 2.51482564]]), array([[1.79430689, 0.        ],
        [0.89715344, 2.3736449 ]]), array([[3.38834229, 0.        ],
        [1.69417115, 4.48235553]]), array([[2.69497696, 0.        ],
        [1.34748848, 3.56511941]]), array([[0.75232746, 0.        ],
        [0.37616373, 0.99523568]]), array([[2.41182343, 0.        ],
        [1.20591172, 3.19054251]]), array([[2.19427325, 0.        ],
        [1.09713662, 2.90275066]]), array([[1.8204996, 0.       ],
        [0.9102498, 2.4082946]]), array([[3.33448863, 0.        ],
        [1.66724431, 4.41111383]]), array([[1.44741944, 0.        ],
        [0.72370972, 1.91475594]]), array([[3.1605429 , 0.        ],
        [1.58027145, 4.18100527]]), array([[1.55759658, 0.        ],
        [0.77879829, 2.06050659]]), ...]
In [1758]:
w = np.eye(3)
g = np.eye(3)
w[1:,1:] = np.eye(2) * 0


test = g.dot(w).dot(g.T)
test
Out[1758]:
array([[1., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]])
In [772]:
np.linalg.inv(np.eye(2))
Out[772]:
array([[1., 0.],
       [0., 1.]])
In [253]:
dat.head()
Out[253]:
2018-01-16 12:25:13 b1fc2b93-54b3-4503-b3cb-1c65263ff0e5 eow.alc.co.jp https://eow.alc.co.jp/search Windows 8 PC YONE_NETWORK_IMP 0 0.1 0.2 221.88.239.234
0 2018-01-16 12:25:13 2c5c7541-9d70-41a9-8ac7-742fc7b44c8d adroute.focas.jp https://adroute.focas.jp/ads/show_page.html Android SD YONE_NETWORK_IMP 0 0 0 106.131.7.80
1 2018-01-16 12:25:13 1032a1ba-89d1-4b5d-a48c-ff1431fef476 eow.alc.co.jp https://eow.alc.co.jp/search Windows 10 PC YONE_NETWORK_IMP 0 0 0 119.104.64.237
2 2018-01-16 12:25:13 cccb41df-79bc-4cd3-9a35-2d9cd1a81c04 www.pixiv.net https://www.pixiv.net/ads_frame.php iPhone SD YONE_NETWORK_IMP 0 0 0 106.130.45.169
3 2018-01-16 12:25:13 d3ca458b-7400-4c23-aacb-3646c8d9b7bb www.mapion.co.jp https://www.mapion.co.jp/address/38 Windows 7 PC YONE_NETWORK_IMP 0 0 0 180.20.157.249
4 2018-01-16 12:25:13 f4ba7b3b-b518-455b-b941-cc1bf0c9437e news.livedoor.com http://news.livedoor.com/topics/detail/14163467 Windows 7 PC YONE_NETWORK_IMP 0 0 0 210.175.4.228
In [255]:
dat = pd.read_csv("/home/practice_data/folder2/test_file.gz", compression='gzip', sep='\t')
dat
---------------------------------------------------------------------------
ParserError                               Traceback (most recent call last)
<ipython-input-255-03e69225b798> in <module>
----> 1 dat = pd.read_csv("/home/practice_data/folder2/test_file.gz", compression='gzip')
      2 dat

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    700                     skip_blank_lines=skip_blank_lines)
    701 
--> 702         return _read(filepath_or_buffer, kwds)
    703 
    704     parser_f.__name__ = name

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    433 
    434     try:
--> 435         data = parser.read(nrows)
    436     finally:
    437         parser.close()

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
   1137     def read(self, nrows=None):
   1138         nrows = _validate_integer('nrows', nrows)
-> 1139         ret = self._engine.read(nrows)
   1140 
   1141         # May alter columns / col_dict

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
   1993     def read(self, nrows=None):
   1994         try:
-> 1995             data = self._reader.read(nrows)
   1996         except StopIteration:
   1997             if self._first_chunk:

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()

pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()

ParserError: Error tokenizing data. C error: Expected 1 fields in line 4069, saw 3
In [619]:
import gzip
FILES = glob.glob('/home/dacdata/2021/dataset/daclog/*')
stop = 100
count = 0
dat = []

def makedat(file, test=False, count = 100):
    with gzip.open(file,'r') as file:
        count = 0
        for line in file:
            l = line.decode('utf-8').split(',')
            ll = len(l)
            dat.append(l[:13]) #前の13列のみ取る

            if count == stop and test:
                break

            count += 1
    return pd.DataFrame(dat)
    
makedat(FILES[0], True)
Out[619]:
0 1 2 3 4 5 6 7 8 9 10 11 12
0 2020-01-01 00:01:13 bd09a0017d1ec7551c7762afa87ec0cb https://www.daily.co.jp/ring/2019/12/31/001300... www.daily.co.jp ring 2019 Mobile Safari 4.0 Android 7.1.1 SD 119.230.172.115 Mozilla/5.0 (Linux; Android 7.1.1; F-05J Build...
1 2020-01-01 00:03:46 4d6473e2fea45cc88a9835c65243bd27 https://www.sponichi.co.jp/entertainment/news/... www.sponichi.co.jp entertainment news Chrome 71.0.3578.99 Android 8.0.0 SD 118.19.112.219 Mozilla/5.0 (Linux; Android 8.0.0; SC-04J) App...
2 2020-01-01 00:05:21 b4e800c1b68a98bee0d3c72218124f76 https://jp-tags.mediaforge.com/pix/4462 jp-tags.mediaforge.com pix 4462 IE 11.0 Windows 10 NT 10.0 PC 163.58.84.81 Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7...
3 2020-01-01 00:08:28 0c0718f614cdb1e39d7a41191e4eb9d4 https://hochi.news/articles/20191230-OHT1T5021... hochi.news articles 20191230-OHT1T50214.html IE 11.0 Windows 10 NT 10.0 PC 219.208.3.27 Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7...
4 2020-01-01 00:10:47 b5d7be1d7d8d576c34da84e91e12751b https://ciatr.jp/topics/44182 ciatr.jp topics 44182 Chrome 64.0.3282.112 iOS 11.2 SD 58.189.68.114 Mozilla/5.0 (iPhone; CPU iPhone OS 11_2 like M...
5 2020-01-01 00:13:12 d0b5a9d86ced2f4aeed0039a83b58802 https://jp.sharp/products/shv40 jp.sharp products shv40 Chrome 79.0.3945.88 Windows 10 NT 10.0 PC 153.130.146.188 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
6 2020-01-01 00:15:05 22ea87d3a96bbc1ef0971579b13e13c2 https://beauty.hotpepper.jp/kr/slnH000399145/c... beauty.hotpepper.jp kr slnH000399145 Safari UNKNOWN iOS 13.3 SD 180.57.152.98 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
7 2020-01-01 00:00:50 3f0f632888a678ce215d825efe1215b0 https://www.daily.co.jp/gossip/2019/04/05/0012... www.daily.co.jp gossip 2019 Mobile Safari 4.0 Android SD 106.172.58.196 Mozilla/5.0 (Linux; Android 9; SO-02L Build/53...
8 2020-01-01 00:01:43 7fbbed4be888012042255ec7660b18e5 https://s.cosme.net/product/product_id/1017311... s.cosme.net product product_id Mobile Safari UNKNOWN iOS 13.1.2 SD 36.8.104.58 Mozilla/5.0 (iPhone; CPU iPhone OS 13_1_2 like...
9 2020-01-01 00:04:38 60724e5e1e6583c3ea6255e87c4fdcec https://news.mixi.jp/view_news.pl news.mixi.jp view_news.pl Other UNKNOWN Other XX 74.125.41.8 GoogleAdExchange\n
10 2020-01-01 00:06:57 1e4e56dd3271158df2e6fbea0da32bb5 https://s.cosme.net/brand/brand_id/4656/review... s.cosme.net brand brand_id Chrome 79.0.3945.93 Android 8.0.0 SD 27.142.62.201 Mozilla/5.0 (Linux; Android 8.0.0; SOV35) Appl...
11 2020-01-01 00:07:40 d20cc7e92bc9ab4cba3c011cb6a7d6e0 https://ima.goo.ne.jp/word/166532/%E6%9C%A8%E4... ima.goo.ne.jp word 166532 Other UNKNOWN Other XX 74.125.41.20 GoogleAdExchange\n
12 2020-01-01 00:08:20 d267b6575ea041552bddea88e986b960 https://xn--eckwa2aa3a9c8j8bve9d.gamewith.jp/a... xn--eckwa2aa3a9c8j8bve9d.gamewith.jp article show Safari UNKNOWN iOS 13.3 SD 106.133.125.223 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
13 2020-01-01 00:09:15 d20cc7e92bc9ab4cba3c011cb6a7d6e0 https://ima.goo.ne.jp/word/166495/%E3%80%8E%EF... ima.goo.ne.jp word 166495 Edge 18.17763 Windows 10 NT 10.0 PC 160.248.175.42 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
14 2020-01-01 00:10:52 1e4e56dd3271158df2e6fbea0da32bb5 https://s.cosme.net/brand/brand_id/4656/review... s.cosme.net brand brand_id Chrome 79.0.3945.93 Android 8.0.0 SD 27.142.62.201 Mozilla/5.0 (Linux; Android 8.0.0; SOV35) Appl...
15 2020-01-01 00:01:36 b9872961ee964842ca6ad658ca5f84c1 https://seesaawiki.jp/w/kobayak/d/MenuBar2 seesaawiki.jp w kobayak Edge 18.18363 Windows 10 NT 10.0 PC 126.189.10.123 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
16 2020-01-01 00:05:38 f9baaf9ba03435a51b863a5f3e6058dd https://mainichikirei.jp/photo/20191230dog00m1... mainichikirei.jp photo 20191230dog00m100022000c.html Other UNKNOWN Other XX 172.217.42.13 GoogleAdExchange\n
17 2020-01-01 00:08:23 b9872961ee964842ca6ad658ca5f84c1 https://seesaawiki.jp/w/kobayak/d/FrontPage seesaawiki.jp w kobayak Edge 18.18363 Windows 10 NT 10.0 PC 126.189.10.123 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
18 2020-01-01 00:10:50 0abb404555ed13d4a310818f7b7030c9 https://www.biglobe.ne.jp www.biglobe.ne.jp Chrome 78.0.3904.108 Windows 7 NT 6.1 PC 126.165.232.229 Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.3...
19 2020-01-01 00:13:22 21cf585c087e378d1955c3f40842de60 https://clicccar.com/2019/12/31/943176 clicccar.com 2019 12 Safari UNKNOWN iOS 13.3 SD 126.3.36.124 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
20 2020-01-01 00:15:19 ddfa33282ec6fe085791aced568843a2 https://dot.asahi.com/wa/2019030600009.html dot.asahi.com wa 2019030600009.html IE 11.0 Windows 8.1 NT 6.3 PC 119.173.211.154 Mozilla/5.0 (Windows NT 6.3; WOW64; Trident/7....
21 2020-01-01 00:16:37 7e18907bed26d12f974172733864ad30 https://hochi.news/articles/20180113-OHT1T5017... hochi.news articles 20180113-OHT1T50171.html Mobile Safari 4.0 Android 8.0.0 SD 118.158.63.157 Mozilla/5.0 (Linux; Android 8.0.0; SOV35 Build...
22 2020-01-01 00:18:18 823ca623b1db569dc66e43b1774d40a9 https://www2.nissan.co.jp/SP/X-TRAIL/PROPILOT-... www2.nissan.co.jp SP X-TRAIL Chrome 79.0.3945.93 Android SD 113.158.67.211 Mozilla/5.0 (Linux; Android 9; SOV39) AppleWeb...
23 2020-01-01 00:19:28 e82c596511dac0fd0df2d66a4f312078 https://game8.jp/dq8/98507 game8.jp dq8 98507 Chrome 78.0.3904.62 Android SD 49.98.170.58 Mozilla/5.0 (Linux; Android 9; F-02L) AppleWeb...
24 2020-01-01 00:20:33 ec8d926215f965d45db2ac123a51e2a9 https://www.nikkansports.com/m/sports/golf/new... www.nikkansports.com m sports Mobile Safari UNKNOWN iOS 10.3.3 SD 106.130.138.42 Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_3 like...
25 2020-01-01 00:01:10 ffdad26a110999fd09a2e1e7428e45a4 https://cookpad.com/recipe/2246289 cookpad.com recipe 2246289 Safari UNKNOWN iOS 13.3 SD 106.181.207.18 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
26 2020-01-01 00:04:07 7d13152f7e15b78929b0717351e5ec72 https://www.nttdocomo.co.jp/mydocomo/data www.nttdocomo.co.jp mydocomo data Chrome 79.0.3945.93 Android SD 49.98.79.70 Mozilla/5.0 (Linux; Android 9; SH-01K) AppleWe...
27 2020-01-01 00:06:24 d0af0262231e41ef8a8fc1ba6f537313 https://www.nikkansports.com/m/entertainment/n... www.nikkansports.com m entertainment Chrome 79.0.3945.93 Android SD 202.208.47.217 Mozilla/5.0 (Linux; Android 9; MAR-LX2J) Apple...
28 2020-01-01 00:08:38 83c22176119113445e551eec438452eb https://wear.jp/sp/brand/carhartt/jacket-outer... wear.jp sp brand Safari UNKNOWN iOS 11.4 SD 126.153.5.188 Mozilla/5.0 (iPhone; CPU iPhone OS 11_4 like M...
29 2020-01-01 00:11:38 8b765e51cc41db9588b3a4f28f238131 https://www.ocn.ne.jp www.ocn.ne.jp Edge 18.18362 Windows 10 NT 10.0 PC 220.98.80.236 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
... ... ... ... ... ... ... ... ... ... ... ... ... ...
71 2020-01-01 00:15:49 1e59e6da097c59e760ec0e601bcc396f https://tpc.googlesyndication.com/safeframe/1-... tpc.googlesyndication.com safeframe 1-0-37 Edge 18.18362 Windows 10 NT 10.0 PC 116.70.137.0 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
72 2020-01-01 00:18:17 0eaad2308b4424546616530110089e2a https://beauty.hotpepper.jp/relax/svcSD/macDA/... beauty.hotpepper.jp relax svcSD Chrome 79.0.3945.93 Android 7.0 SD 106.180.11.28 Mozilla/5.0 (Linux; Android 7.0; KOB-W09) Appl...
73 2020-01-01 00:02:29 b1d2485d009089d7cf1377635826d9ba https://dot.asahi.com/dot/2019042600016.html dot.asahi.com dot 2019042600016.html Chrome 79.0.3945.88 Windows 7 NT 6.1 PC 126.220.145.31 Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...
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80 2020-01-01 00:02:59 787c209a1c6bed18261638a05f362092 https://motor-fan.jp/article/10010542 motor-fan.jp article 10010542 Other UNKNOWN Other XX 172.217.42.4 GoogleAdExchange\n
81 2020-01-01 00:07:21 93295385ce827f5671aba696c05c4bf3 https://blog.goo.ne.jp/sasakajapan/e/3307b9c36... blog.goo.ne.jp sasakajapan e Safari 13.0.4 iOS 13.3 SD 115.163.142.169 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
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88 2020-01-01 00:00:57 d89255693d85d7307bd9a69058a417fc http://pc-play.games.dmm.com/play/kanpani pc-play.games.dmm.com play kanpani Chrome 79.0.3945.88 Windows 10 NT 10.0 PC 202.222.44.132 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
89 2020-01-01 00:03:33 73144efde6658c1eb5b1bddfa326b258 https://www.nikkansports.com/m/battle/news/201... www.nikkansports.com m battle Mobile Safari 4.0 Android SD 1.75.253.98 Mozilla/5.0 (Linux; Android 9; SC-02K Build/PP...
90 2020-01-01 00:05:28 17368e9fc75919490a6386d9be03eee4 https://www.nikkansports.com/m/battle/photonew... www.nikkansports.com m battle Mobile Safari 4.0 Android SD 126.34.24.173 Mozilla/5.0 (Linux; Android 9; 605SH Build/S20...
91 2020-01-01 00:07:04 70f268c38f7c2338ce26b2b40bdea4c0 https://www.sponichi.co.jp www.sponichi.co.jp Chrome 79.0.3945.93 Android 5.0 SD 110.135.90.171 Mozilla/5.0 (Linux; Android 5.0; SC-02F) Apple...
92 2020-01-01 00:08:48 2541bb5cd8041baeb1b57982bfc93864 https://d-30935329663809372655.ampproject.net d-30935329663809372655.ampproject.net Mobile Safari UNKNOWN iOS 13.3 SD 49.98.137.210 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
93 2020-01-01 00:11:25 be7b2d087193b776110acd056cab4363 https://suumo.jp/sp/chukomansion/tokyo/sc suumo.jp sp chukomansion Chrome 79.0.3945.73 iOS 13.3 SD 150.249.3.78 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
94 2020-01-01 00:00:23 c19045bd49b4764f62e4fdb6da96f48c https://ssl.tour-sys.com/travel-west/search.php ssl.tour-sys.com travel-west search.php Edge 18.18362 Windows 10 NT 10.0 PC 219.160.220.110 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
95 2020-01-01 00:04:18 1bb184068d8795960b66a4477fbda86a https://jp-tags.rd.linksynergy.com/pix/1032 jp-tags.rd.linksynergy.com pix 1032 Chrome 74.0.3729.136 Android SD 1.75.1.80 Mozilla/5.0 (Linux; Android 9; L-03K) AppleWeb...
96 2020-01-01 00:08:10 b74d4d5ff5879229682699b5f7bd6f43 https://kakakumag.com/av-kaden kakakumag.com av-kaden Other UNKNOWN Other XX 74.125.41.6 GoogleAdExchange\n
97 2020-01-01 00:10:36 b560bfddbc84870b26e47a9e9cf092f1 https://www.nikkansports.com/m/sports/news/201... www.nikkansports.com m sports Chrome 79.0.3945.93 Android SD 106.130.50.50 Mozilla/5.0 (Linux; Android 9; KYV44) AppleWeb...
98 2020-01-01 00:11:36 470bf2abb3e0d3fa30e0ed73b2a9df97 https://jp-tags.rd.linksynergy.com/pix/1032 jp-tags.rd.linksynergy.com pix 1032 Edge 18.18362 Windows 10 NT 10.0 PC 121.95.33.180 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
99 2020-01-01 00:12:40 1446f7677a57b09e05c2679bdced3fa3 https://iko-yo.net/events iko-yo.net events Mobile Safari 4.0 Android SD 133.201.206.32 Mozilla/5.0 (Linux; Android 9; SC-04L Build/PP...
100 2020-01-01 00:14:38 7989dd9c1606f8e66e801a41b5fff49e https://www.daily.co.jp/newsflash/gossip/2015/... www.daily.co.jp newsflash gossip Safari 13.0.4 iOS 13.3 SD 49.98.17.143 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...

101 rows × 13 columns

In [8]:
import gzip
import pandas as pd
import glob
import gzip

FILES = glob.glob('/home/dacdata/2021/dataset/daclog/*')

def makedat(file, test=False, stop = 100):
    dat = []
    with gzip.open(file,'r') as file:
        count = 0
        for line in file:
            l = line.decode('utf-8').split(',')
            ll = len(l)
            dat.append(l[:13]) #前の13列のみ取る
            if count == stop and test:
                break

            count += 1
    return pd.DataFrame(dat)
    
makedat(FILES[0], True)
Out[8]:
0 1 2 3 4 5 6 7 8 9 10 11 12
0 2020-01-01 00:01:13 bd09a0017d1ec7551c7762afa87ec0cb https://www.daily.co.jp/ring/2019/12/31/001300... www.daily.co.jp ring 2019 Mobile Safari 4.0 Android 7.1.1 SD 119.230.172.115 Mozilla/5.0 (Linux; Android 7.1.1; F-05J Build...
1 2020-01-01 00:03:46 4d6473e2fea45cc88a9835c65243bd27 https://www.sponichi.co.jp/entertainment/news/... www.sponichi.co.jp entertainment news Chrome 71.0.3578.99 Android 8.0.0 SD 118.19.112.219 Mozilla/5.0 (Linux; Android 8.0.0; SC-04J) App...
2 2020-01-01 00:05:21 b4e800c1b68a98bee0d3c72218124f76 https://jp-tags.mediaforge.com/pix/4462 jp-tags.mediaforge.com pix 4462 IE 11.0 Windows 10 NT 10.0 PC 163.58.84.81 Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7...
3 2020-01-01 00:08:28 0c0718f614cdb1e39d7a41191e4eb9d4 https://hochi.news/articles/20191230-OHT1T5021... hochi.news articles 20191230-OHT1T50214.html IE 11.0 Windows 10 NT 10.0 PC 219.208.3.27 Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7...
4 2020-01-01 00:10:47 b5d7be1d7d8d576c34da84e91e12751b https://ciatr.jp/topics/44182 ciatr.jp topics 44182 Chrome 64.0.3282.112 iOS 11.2 SD 58.189.68.114 Mozilla/5.0 (iPhone; CPU iPhone OS 11_2 like M...
5 2020-01-01 00:13:12 d0b5a9d86ced2f4aeed0039a83b58802 https://jp.sharp/products/shv40 jp.sharp products shv40 Chrome 79.0.3945.88 Windows 10 NT 10.0 PC 153.130.146.188 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
6 2020-01-01 00:15:05 22ea87d3a96bbc1ef0971579b13e13c2 https://beauty.hotpepper.jp/kr/slnH000399145/c... beauty.hotpepper.jp kr slnH000399145 Safari UNKNOWN iOS 13.3 SD 180.57.152.98 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
7 2020-01-01 00:00:50 3f0f632888a678ce215d825efe1215b0 https://www.daily.co.jp/gossip/2019/04/05/0012... www.daily.co.jp gossip 2019 Mobile Safari 4.0 Android SD 106.172.58.196 Mozilla/5.0 (Linux; Android 9; SO-02L Build/53...
8 2020-01-01 00:01:43 7fbbed4be888012042255ec7660b18e5 https://s.cosme.net/product/product_id/1017311... s.cosme.net product product_id Mobile Safari UNKNOWN iOS 13.1.2 SD 36.8.104.58 Mozilla/5.0 (iPhone; CPU iPhone OS 13_1_2 like...
9 2020-01-01 00:04:38 60724e5e1e6583c3ea6255e87c4fdcec https://news.mixi.jp/view_news.pl news.mixi.jp view_news.pl Other UNKNOWN Other XX 74.125.41.8 GoogleAdExchange\n
10 2020-01-01 00:06:57 1e4e56dd3271158df2e6fbea0da32bb5 https://s.cosme.net/brand/brand_id/4656/review... s.cosme.net brand brand_id Chrome 79.0.3945.93 Android 8.0.0 SD 27.142.62.201 Mozilla/5.0 (Linux; Android 8.0.0; SOV35) Appl...
11 2020-01-01 00:07:40 d20cc7e92bc9ab4cba3c011cb6a7d6e0 https://ima.goo.ne.jp/word/166532/%E6%9C%A8%E4... ima.goo.ne.jp word 166532 Other UNKNOWN Other XX 74.125.41.20 GoogleAdExchange\n
12 2020-01-01 00:08:20 d267b6575ea041552bddea88e986b960 https://xn--eckwa2aa3a9c8j8bve9d.gamewith.jp/a... xn--eckwa2aa3a9c8j8bve9d.gamewith.jp article show Safari UNKNOWN iOS 13.3 SD 106.133.125.223 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
13 2020-01-01 00:09:15 d20cc7e92bc9ab4cba3c011cb6a7d6e0 https://ima.goo.ne.jp/word/166495/%E3%80%8E%EF... ima.goo.ne.jp word 166495 Edge 18.17763 Windows 10 NT 10.0 PC 160.248.175.42 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
14 2020-01-01 00:10:52 1e4e56dd3271158df2e6fbea0da32bb5 https://s.cosme.net/brand/brand_id/4656/review... s.cosme.net brand brand_id Chrome 79.0.3945.93 Android 8.0.0 SD 27.142.62.201 Mozilla/5.0 (Linux; Android 8.0.0; SOV35) Appl...
15 2020-01-01 00:01:36 b9872961ee964842ca6ad658ca5f84c1 https://seesaawiki.jp/w/kobayak/d/MenuBar2 seesaawiki.jp w kobayak Edge 18.18363 Windows 10 NT 10.0 PC 126.189.10.123 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
16 2020-01-01 00:05:38 f9baaf9ba03435a51b863a5f3e6058dd https://mainichikirei.jp/photo/20191230dog00m1... mainichikirei.jp photo 20191230dog00m100022000c.html Other UNKNOWN Other XX 172.217.42.13 GoogleAdExchange\n
17 2020-01-01 00:08:23 b9872961ee964842ca6ad658ca5f84c1 https://seesaawiki.jp/w/kobayak/d/FrontPage seesaawiki.jp w kobayak Edge 18.18363 Windows 10 NT 10.0 PC 126.189.10.123 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
18 2020-01-01 00:10:50 0abb404555ed13d4a310818f7b7030c9 https://www.biglobe.ne.jp www.biglobe.ne.jp Chrome 78.0.3904.108 Windows 7 NT 6.1 PC 126.165.232.229 Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.3...
19 2020-01-01 00:13:22 21cf585c087e378d1955c3f40842de60 https://clicccar.com/2019/12/31/943176 clicccar.com 2019 12 Safari UNKNOWN iOS 13.3 SD 126.3.36.124 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
20 2020-01-01 00:15:19 ddfa33282ec6fe085791aced568843a2 https://dot.asahi.com/wa/2019030600009.html dot.asahi.com wa 2019030600009.html IE 11.0 Windows 8.1 NT 6.3 PC 119.173.211.154 Mozilla/5.0 (Windows NT 6.3; WOW64; Trident/7....
21 2020-01-01 00:16:37 7e18907bed26d12f974172733864ad30 https://hochi.news/articles/20180113-OHT1T5017... hochi.news articles 20180113-OHT1T50171.html Mobile Safari 4.0 Android 8.0.0 SD 118.158.63.157 Mozilla/5.0 (Linux; Android 8.0.0; SOV35 Build...
22 2020-01-01 00:18:18 823ca623b1db569dc66e43b1774d40a9 https://www2.nissan.co.jp/SP/X-TRAIL/PROPILOT-... www2.nissan.co.jp SP X-TRAIL Chrome 79.0.3945.93 Android SD 113.158.67.211 Mozilla/5.0 (Linux; Android 9; SOV39) AppleWeb...
23 2020-01-01 00:19:28 e82c596511dac0fd0df2d66a4f312078 https://game8.jp/dq8/98507 game8.jp dq8 98507 Chrome 78.0.3904.62 Android SD 49.98.170.58 Mozilla/5.0 (Linux; Android 9; F-02L) AppleWeb...
24 2020-01-01 00:20:33 ec8d926215f965d45db2ac123a51e2a9 https://www.nikkansports.com/m/sports/golf/new... www.nikkansports.com m sports Mobile Safari UNKNOWN iOS 10.3.3 SD 106.130.138.42 Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_3 like...
25 2020-01-01 00:01:10 ffdad26a110999fd09a2e1e7428e45a4 https://cookpad.com/recipe/2246289 cookpad.com recipe 2246289 Safari UNKNOWN iOS 13.3 SD 106.181.207.18 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
26 2020-01-01 00:04:07 7d13152f7e15b78929b0717351e5ec72 https://www.nttdocomo.co.jp/mydocomo/data www.nttdocomo.co.jp mydocomo data Chrome 79.0.3945.93 Android SD 49.98.79.70 Mozilla/5.0 (Linux; Android 9; SH-01K) AppleWe...
27 2020-01-01 00:06:24 d0af0262231e41ef8a8fc1ba6f537313 https://www.nikkansports.com/m/entertainment/n... www.nikkansports.com m entertainment Chrome 79.0.3945.93 Android SD 202.208.47.217 Mozilla/5.0 (Linux; Android 9; MAR-LX2J) Apple...
28 2020-01-01 00:08:38 83c22176119113445e551eec438452eb https://wear.jp/sp/brand/carhartt/jacket-outer... wear.jp sp brand Safari UNKNOWN iOS 11.4 SD 126.153.5.188 Mozilla/5.0 (iPhone; CPU iPhone OS 11_4 like M...
29 2020-01-01 00:11:38 8b765e51cc41db9588b3a4f28f238131 https://www.ocn.ne.jp www.ocn.ne.jp Edge 18.18362 Windows 10 NT 10.0 PC 220.98.80.236 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
... ... ... ... ... ... ... ... ... ... ... ... ... ...
71 2020-01-01 00:15:49 1e59e6da097c59e760ec0e601bcc396f https://tpc.googlesyndication.com/safeframe/1-... tpc.googlesyndication.com safeframe 1-0-37 Edge 18.18362 Windows 10 NT 10.0 PC 116.70.137.0 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
72 2020-01-01 00:18:17 0eaad2308b4424546616530110089e2a https://beauty.hotpepper.jp/relax/svcSD/macDA/... beauty.hotpepper.jp relax svcSD Chrome 79.0.3945.93 Android 7.0 SD 106.180.11.28 Mozilla/5.0 (Linux; Android 7.0; KOB-W09) Appl...
73 2020-01-01 00:02:29 b1d2485d009089d7cf1377635826d9ba https://dot.asahi.com/dot/2019042600016.html dot.asahi.com dot 2019042600016.html Chrome 79.0.3945.88 Windows 7 NT 6.1 PC 126.220.145.31 Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...
74 2020-01-01 00:04:54 b1d2485d009089d7cf1377635826d9ba https://dot.asahi.com/dot/2019042600016.html dot.asahi.com dot 2019042600016.html Chrome 79.0.3945.88 Windows 7 NT 6.1 PC 126.220.145.31 Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...
75 2020-01-01 00:07:09 e8da8f675fd1a4801357bb7b98ad9e2e https://blog.goo.ne.jp/mijimiji_0401/e/3bd4c66... blog.goo.ne.jp mijimiji_0401 e Chrome 77.0.3865.116 Android 7.1.2 SD 153.251.199.240 Mozilla/5.0 (Linux; Android 7.1.2; G1701) Appl...
76 2020-01-01 00:08:46 678fd3a513806b27c4cb4ee6ea1617ea https://www.nikkansports.com/m/entertainment/n... www.nikkansports.com m entertainment Chrome 72.0.3626.121 Android SD 126.162.38.137 Mozilla/5.0 (Linux; Android 9; SHV40_u) AppleW...
77 2020-01-01 00:11:49 839db8d09102a972b2c4be3c83aff7a5 https://www.au.com/mobile/product/smartphone/s... www.au.com mobile product Chrome 70.0.3538.110 Android 8.1.0 SD 1.75.241.228 Mozilla/5.0 (Linux; Android 8.1.0; F-01J) Appl...
78 2020-01-01 00:13:54 b244b0565a3c4ddb647ffefb954454be https://dot.asahi.com/wa/2019123100013.html dot.asahi.com wa 2019123100013.html Chrome 79.0.3945.93 Android SD 180.199.100.206 Mozilla/5.0 (Linux; Android 9; SOV37) AppleWeb...
79 2020-01-01 00:16:04 9e2d5be7cd69016cfac6a2f1daa60d75 https://jp-tags.rd.linksynergy.com/pix/1032 jp-tags.rd.linksynergy.com pix 1032 Chrome 79.0.3945.93 Android SD 106.73.135.160 Mozilla/5.0 (Linux; Android 9; SC-01K) AppleWe...
80 2020-01-01 00:02:59 787c209a1c6bed18261638a05f362092 https://motor-fan.jp/article/10010542 motor-fan.jp article 10010542 Other UNKNOWN Other XX 172.217.42.4 GoogleAdExchange\n
81 2020-01-01 00:07:21 93295385ce827f5671aba696c05c4bf3 https://blog.goo.ne.jp/sasakajapan/e/3307b9c36... blog.goo.ne.jp sasakajapan e Safari 13.0.4 iOS 13.3 SD 115.163.142.169 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
82 2020-01-01 00:08:45 983499ce411471b07e906b51d146ae96 https://www.chunichi.co.jp/chuspo/article/ente... www.chunichi.co.jp chuspo article Mobile Safari UNKNOWN iOS 13.1.3 SD 61.22.129.242 Mozilla/5.0 (iPhone; CPU iPhone OS 13_1_3 like...
83 2020-01-01 00:10:58 4728f6a29a338c52e6fbbf55e719dd91 https://syosetu.org/search syosetu.org search Chrome 79.0.3945.88 Windows 10 NT 10.0 PC 153.231.168.234 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
84 2020-01-01 00:14:27 6ada7047884ec7a0bbadc39a8ae7db22 https://tv.yahoo.co.jp/listings/bs1 tv.yahoo.co.jp listings bs1 Chrome 56.0.2924.87 Android 6.0 SD 1.75.238.219 Mozilla/5.0 (Linux; Android 6.0; d-01J Build/H...
85 2020-01-01 00:16:49 4d7fa3d8a7e0db9aad59b3c0fb7f4a53 https://gakumado.mynavi.jp/gmd/diagnoses/32820... gakumado.mynavi.jp gmd diagnoses Chrome 79.0.3945.93 Android 7.0 SD 27.81.181.214 Mozilla/5.0 (Linux; Android 7.0; SH-02J) Apple...
86 2020-01-01 00:18:14 37c88731bd0e9801387dc92856b1ae11 https://ima.goo.ne.jp/word/166478/%E3%81%8D%E3... ima.goo.ne.jp word 166478 Mobile Safari 4.0 Android 6.0.1 SD 1.75.1.23 Mozilla/5.0 (Linux; Android 6.0.1; F-01H Build...
87 2020-01-01 00:20:05 39bd4a3aa359053134e1ba2a2a978a1a https://s.kakaku.com/item/K0001159324/images s.kakaku.com item K0001159324 Chrome 79.0.3945.93 Android SD 221.185.203.125 Mozilla/5.0 (Linux; Android 9; SO-01K) AppleWe...
88 2020-01-01 00:00:57 d89255693d85d7307bd9a69058a417fc http://pc-play.games.dmm.com/play/kanpani pc-play.games.dmm.com play kanpani Chrome 79.0.3945.88 Windows 10 NT 10.0 PC 202.222.44.132 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
89 2020-01-01 00:03:33 73144efde6658c1eb5b1bddfa326b258 https://www.nikkansports.com/m/battle/news/201... www.nikkansports.com m battle Mobile Safari 4.0 Android SD 1.75.253.98 Mozilla/5.0 (Linux; Android 9; SC-02K Build/PP...
90 2020-01-01 00:05:28 17368e9fc75919490a6386d9be03eee4 https://www.nikkansports.com/m/battle/photonew... www.nikkansports.com m battle Mobile Safari 4.0 Android SD 126.34.24.173 Mozilla/5.0 (Linux; Android 9; 605SH Build/S20...
91 2020-01-01 00:07:04 70f268c38f7c2338ce26b2b40bdea4c0 https://www.sponichi.co.jp www.sponichi.co.jp Chrome 79.0.3945.93 Android 5.0 SD 110.135.90.171 Mozilla/5.0 (Linux; Android 5.0; SC-02F) Apple...
92 2020-01-01 00:08:48 2541bb5cd8041baeb1b57982bfc93864 https://d-30935329663809372655.ampproject.net d-30935329663809372655.ampproject.net Mobile Safari UNKNOWN iOS 13.3 SD 49.98.137.210 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
93 2020-01-01 00:11:25 be7b2d087193b776110acd056cab4363 https://suumo.jp/sp/chukomansion/tokyo/sc suumo.jp sp chukomansion Chrome 79.0.3945.73 iOS 13.3 SD 150.249.3.78 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...
94 2020-01-01 00:00:23 c19045bd49b4764f62e4fdb6da96f48c https://ssl.tour-sys.com/travel-west/search.php ssl.tour-sys.com travel-west search.php Edge 18.18362 Windows 10 NT 10.0 PC 219.160.220.110 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
95 2020-01-01 00:04:18 1bb184068d8795960b66a4477fbda86a https://jp-tags.rd.linksynergy.com/pix/1032 jp-tags.rd.linksynergy.com pix 1032 Chrome 74.0.3729.136 Android SD 1.75.1.80 Mozilla/5.0 (Linux; Android 9; L-03K) AppleWeb...
96 2020-01-01 00:08:10 b74d4d5ff5879229682699b5f7bd6f43 https://kakakumag.com/av-kaden kakakumag.com av-kaden Other UNKNOWN Other XX 74.125.41.6 GoogleAdExchange\n
97 2020-01-01 00:10:36 b560bfddbc84870b26e47a9e9cf092f1 https://www.nikkansports.com/m/sports/news/201... www.nikkansports.com m sports Chrome 79.0.3945.93 Android SD 106.130.50.50 Mozilla/5.0 (Linux; Android 9; KYV44) AppleWeb...
98 2020-01-01 00:11:36 470bf2abb3e0d3fa30e0ed73b2a9df97 https://jp-tags.rd.linksynergy.com/pix/1032 jp-tags.rd.linksynergy.com pix 1032 Edge 18.18362 Windows 10 NT 10.0 PC 121.95.33.180 Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...
99 2020-01-01 00:12:40 1446f7677a57b09e05c2679bdced3fa3 https://iko-yo.net/events iko-yo.net events Mobile Safari 4.0 Android SD 133.201.206.32 Mozilla/5.0 (Linux; Android 9; SC-04L Build/PP...
100 2020-01-01 00:14:38 7989dd9c1606f8e66e801a41b5fff49e https://www.daily.co.jp/newsflash/gossip/2015/... www.daily.co.jp newsflash gossip Safari 13.0.4 iOS 13.3 SD 49.98.17.143 Mozilla/5.0 (iPhone; CPU iPhone OS 13_3 like M...

101 rows × 13 columns

In [ ]:
#cols = ["view_datetime", "user_ID", "purl_subdomain", "purl_base", "purl_path1", "purl_path2", "browser", "browser_version", "os", "os_version", "pc_sd", "ip_address"]
import re
data = pd.read_csv(FILES[0], sep='[^(KHTML)]\,', header=None)
/home/li/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
  This is separate from the ipykernel package so we can avoid doing imports until
In [622]:
data.head()
Out[622]:
view_datetime user_ID purl_subdomain purl_base purl_path1 purl_path2 browser browser_version os os_version pc_sd ip_address
0 2020-01-01 00:01:13 bd09a0017d1ec7551c7762afa87ec0cb https://www.daily.co.jp/ring/2019/12/31/001300... www.daily.co.jp ring 2019 Mobile Safari 4.0 Android 7.1.1 SD 119.230.172.115
1 2020-01-01 00:03:46 4d6473e2fea45cc88a9835c65243bd27 https://www.sponichi.co.jp/entertainment/news/... www.sponichi.co.jp entertainment news Chrome 71.0.3578.99 Android 8.0.0 SD 118.19.112.219
2 2020-01-01 00:05:21 b4e800c1b68a98bee0d3c72218124f76 https://jp-tags.mediaforge.com/pix/4462 jp-tags.mediaforge.com pix 4462 IE 11.0 Windows 10 NT 10.0 PC 163.58.84.81
3 2020-01-01 00:08:28 0c0718f614cdb1e39d7a41191e4eb9d4 https://hochi.news/articles/20191230-OHT1T5021... hochi.news articles 20191230-OHT1T50214.html IE 11.0 Windows 10 NT 10.0 PC 219.208.3.27
4 2020-01-01 00:10:47 b5d7be1d7d8d576c34da84e91e12751b https://ciatr.jp/topics/44182 ciatr.jp topics 44182 Chrome 64.0.3282.112 iOS 11.2 SD 58.189.68.114
In [ ]:
 
In [516]:
 
0 2020-01-01 00:01:13
1 bd09a0017d1ec7551c7762afa87ec0cb
2 https://www.daily.co.jp/ring/2019/12/31/0013002227.shtml
3 www.daily.co.jp
4 ring
5 2019
6 Mobile Safari
7 4.0
8 Android
9 7.1.1
10 SD
11 119.230.172.115
12 Mozilla/5.0 (Linux; Android 7.1.1; F-05J Build/V16R034A; wv) AppleWebKit/537.36 (KHTML
13  like Gecko) Version/4.0 Chrome/79.0.3945.93 Mobile Safari/537.36 YJApp-ANDROID jp.co.yahoo.android.yjtop/3.60.1

0 2020-01-01 00:03:46
1 4d6473e2fea45cc88a9835c65243bd27
2 https://www.sponichi.co.jp/entertainment/news/2019/12/31/kiji/20191231s00041000312000c.html
3 www.sponichi.co.jp
4 entertainment
5 news
6 Chrome
7 71.0.3578.99
8 Android
9 8.0.0
10 SD
11 118.19.112.219
12 Mozilla/5.0 (Linux; Android 8.0.0; SC-04J) AppleWebKit/537.36 (KHTML
13  like Gecko) SamsungBrowser/10.2 Chrome/71.0.3578.99 Mobile Safari/537.36

0 2020-01-01 00:05:21
1 b4e800c1b68a98bee0d3c72218124f76
2 https://jp-tags.mediaforge.com/pix/4462
3 jp-tags.mediaforge.com
4 pix
5 4462
6 IE
7 11.0
8 Windows 10
9 NT 10.0
10 PC
11 163.58.84.81
12 Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko

In [508]:
import pandas as pd
In [276]:
import argparse
import sys
import csv
import glob
import gzip
import tracemalloc


def makedat(file, path, test=False, stop=100):
    print("Start: makedat", file=sys.stderr)
    with open(path,'w',newline='') as f: #Initialize
        pass
    
    with gzip.open(file,'r') as file:
        with open(path,'a',newline='') as f: #Append Mode
            count = 0
            #dat = []
            writer = csv.writer(f)
            for line in file:
                l = line.decode('utf-8').split(',')
                l[12] = ','.join(l[12:]) #前の13列以降を結合する
                #dat.append(l[:13])
                writer.writerow(l)
                if count == stop and test:
                    break
            
                count += 1
            
    print("End: makedat", file=sys.stderr)
    #return pd.DataFrame(dat, columns=COLS_DACLOG)   
daclog_files = glob.glob('/home/dacdata/2021/dataset/daclog/*')
makedat(daclog_files[0], '/home/li/test.csv',True)
Start: makedat
End: makedat
In [517]:
test = pd.read_csv(daclog_files[0],  usecols=[*range(13)], header=None)
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
   1994         try:
-> 1995             data = self._reader.read(nrows)
   1996         except StopIteration:

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()

pandas/_libs/parsers.pyx in pandas._libs.parsers._concatenate_chunks()

~/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/common.py in is_categorical_dtype(arr_or_dtype)
    571 
--> 572 def is_categorical_dtype(arr_or_dtype):
    573     """

KeyboardInterrupt: 

During handling of the above exception, another exception occurred:

KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-517-a23a9905cbfd> in <module>
----> 1 test = pd.read_csv(daclog_files[0],  usecols=[*range(13)], header=None)

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    700                     skip_blank_lines=skip_blank_lines)
    701 
--> 702         return _read(filepath_or_buffer, kwds)
    703 
    704     parser_f.__name__ = name

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    433 
    434     try:
--> 435         data = parser.read(nrows)
    436     finally:
    437         parser.close()

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
   1137     def read(self, nrows=None):
   1138         nrows = _validate_integer('nrows', nrows)
-> 1139         ret = self._engine.read(nrows)
   1140 
   1141         # May alter columns / col_dict

~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
   1993     def read(self, nrows=None):
   1994         try:
-> 1995             data = self._reader.read(nrows)
   1996         except StopIteration:
   1997             if self._first_chunk:

KeyboardInterrupt: 
In [524]:
test = pd.read_csv('/home/dacdata/2021/dataset/prism3/mst_prism3_url_map_000.gz')
In [526]:
test.head(100)
Out[526]:
207338 marblink.co.jp marblink.co.jp/
0 207338 eir-mjs01.com eir-mjs01.com/
1 207378 shopap.lenovo.com shopap.lenovo.com/
2 207381 paku.cocolog-nifty.com paku.cocolog-nifty.com/
3 207399 tworks.hatenablog.jp tworks.hatenablog.jp/
4 207438 peddy-camp.hatenablog.com peddy-camp.hatenablog.com/
5 207478 himika1.com himika1.com/
6 207478 audi107.hatenablog.jp audi107.hatenablog.jp/
7 208107 the-3rd.net the-3rd.net/
8 207438 haveagood.holiday haveagood.holiday/
9 207438 minoh.ooedoonsen.jp minoh.ooedoonsen.jp/
10 207484 hexel.hatenablog.com hexel.hatenablog.com/
11 207484 bellhome.smarthr.jp bellhome.smarthr.jp/
12 207510 mystery.co.jp mystery.co.jp/
13 207522 d-assist.net d-assist.net/
14 208088 jabuddy.hatenablog.com jabuddy.hatenablog.com/
15 208124 wqx543un9u.cocolog-nifty.com wqx543un9u.cocolog-nifty.com/
16 207571 zuboraonnanosyokuji.seesaa.net zuboraonnanosyokuji.seesaa.net/
17 207571 ugzgscdxy2m.hateblo.jp ugzgscdxy2m.hateblo.jp/
18 207438 railway-engineer.hatenablog.com railway-engineer.hatenablog.com/
19 207510 sp.tanteifile.com sp.tanteifile.com/
20 207510 hayai-hotnews.blog.so-net.ne.jp hayai-hotnews.blog.so-net.ne.jp/
21 207510 katagiri.hatenablog.com katagiri.hatenablog.com/
22 207510 tiger-tiger.site tiger-tiger.site/
23 207510 th-block.com th-block.com/
24 207510 yuukikin.cocolog-nifty.com yuukikin.cocolog-nifty.com/
25 207554 4126.buzz-spark.black 4126.buzz-spark.black/
26 207554 ren123456.blog.so-net.ne.jp ren123456.blog.so-net.ne.jp/
27 207554 ao-tori.seesaa.net ao-tori.seesaa.net/
28 207554 redtrendnews.blog.so-net.ne.jp redtrendnews.blog.so-net.ne.jp/
29 207554 ksaclnlskjfslsdkj.cocolog-nifty.com ksaclnlskjfslsdkj.cocolog-nifty.com/
... ... ... ...
70 207636 ykore.hatenablog.com ykore.hatenablog.com/
71 207680 kimagure-koi.xyz kimagure-koi.xyz/
72 207680 internasional.kompas.com internasional.kompas.com/
73 207680 sepia.openmatrix.net sepia.openmatrix.net/
74 207680 news2021-4-14.blogspot.com news2021-4-14.blogspot.com/
75 208088 konamania.exblog.jp konamania.exblog.jp/
76 208088 aroundthirty-dokujyo.hateblo.jp aroundthirty-dokujyo.hateblo.jp/
77 208088 yutori-simple.com yutori-simple.com/
78 208088 miniyagikoyuki.cocolog-nifty.com miniyagikoyuki.cocolog-nifty.com/
79 207960 machinoongakushitsu.hida-ch.com machinoongakushitsu.hida-ch.com/
80 207960 dot.asahi.com dot.asahi.com/aera/2015110400007.html
81 208127 kitakyushubank.co.jp kitakyushubank.co.jp/
82 208130 stock.searchina.ne.jp stock.searchina.ne.jp/data/code.cgi
83 208135 pex.jp pex.jp/amazon_spend/complete?
84 207484 megusoku.com megusoku.com/
85 207487 7462.bazoooka.org 7462.bazoooka.org/
86 207487 healthcare.itmedia.co.jp healthcare.itmedia.co.jp/
87 207506 geinou-today.click geinou-today.click/
88 208088 shouchiku-school.seesaa.net shouchiku-school.seesaa.net/
89 208088 narutaki.hatenablog.com narutaki.hatenablog.com/
90 208089 akai-hideki.hatenablog.com akai-hideki.hatenablog.com/
91 207841 asi17.blog.jp asi17.blog.jp/
92 207841 67404560.at.webry.info 67404560.at.webry.info/
93 208148 www.nurse-agent.com www.nurse-agent.com/
94 207415 midori7614.exblog.jp midori7614.exblog.jp/
95 208035 www.webcg.net www.webcg.net/articles/hiding_WEBCG-impression...
96 208035 response.jp response.jp/special/recent/2800/%E3%82%B9%E3%8...
97 208035 response.jp response.jp/article/2014/8/7/229522.html?
98 208035 response.jp response.jp/article/img/2014/8/21/230421/73813...
99 208035 response.jp response.jp/article/img/2014/8/21/230391/73899...

100 rows × 3 columns

In [528]:
test2 = pd.read_csv('/home/dacdata/2021/dataset/prism3/mst_prism3_v2_purl_base_map_000.gz')
test2.head()
Out[528]:
300686 http://article.auone.jp/detail/1/1/1/102_4_r_20180309_1520547282236685 2018-05-10 00:00:00
0 300042 http://tech.nikkeibp.co.jp/dm/atcl/news/16/031... 2018-05-10 00:00:00
1 300303 http://www.oricon.co.jp/news/2096658 2018-05-10 00:00:00
2 300255 http://ares-news.com/9276/5 2018-05-10 00:00:00
3 300001 http://tech.nikkeibp.co.jp/dm/atcl/news/16/022... 2018-05-10 00:00:00
4 300771 http://kateich.net/1523502623 2018-05-10 00:00:00
In [523]:
 
Out[523]:
207338 marblink.co.jp marblink.co.jp/
0 207338 eir-mjs01.com eir-mjs01.com/
1 207378 shopap.lenovo.com shopap.lenovo.com/
2 207381 paku.cocolog-nifty.com paku.cocolog-nifty.com/
3 207399 tworks.hatenablog.jp tworks.hatenablog.jp/
4 207438 peddy-camp.hatenablog.com peddy-camp.hatenablog.com/
5 207478 himika1.com himika1.com/
6 207478 audi107.hatenablog.jp audi107.hatenablog.jp/
7 208107 the-3rd.net the-3rd.net/
8 207438 haveagood.holiday haveagood.holiday/
9 207438 minoh.ooedoonsen.jp minoh.ooedoonsen.jp/
10 207484 hexel.hatenablog.com hexel.hatenablog.com/
11 207484 bellhome.smarthr.jp bellhome.smarthr.jp/
12 207510 mystery.co.jp mystery.co.jp/
13 207522 d-assist.net d-assist.net/
14 208088 jabuddy.hatenablog.com jabuddy.hatenablog.com/
15 208124 wqx543un9u.cocolog-nifty.com wqx543un9u.cocolog-nifty.com/
16 207571 zuboraonnanosyokuji.seesaa.net zuboraonnanosyokuji.seesaa.net/
17 207571 ugzgscdxy2m.hateblo.jp ugzgscdxy2m.hateblo.jp/
18 207438 railway-engineer.hatenablog.com railway-engineer.hatenablog.com/
19 207510 sp.tanteifile.com sp.tanteifile.com/
20 207510 hayai-hotnews.blog.so-net.ne.jp hayai-hotnews.blog.so-net.ne.jp/
21 207510 katagiri.hatenablog.com katagiri.hatenablog.com/
22 207510 tiger-tiger.site tiger-tiger.site/
23 207510 th-block.com th-block.com/
24 207510 yuukikin.cocolog-nifty.com yuukikin.cocolog-nifty.com/
25 207554 4126.buzz-spark.black 4126.buzz-spark.black/
26 207554 ren123456.blog.so-net.ne.jp ren123456.blog.so-net.ne.jp/
27 207554 ao-tori.seesaa.net ao-tori.seesaa.net/
28 207554 redtrendnews.blog.so-net.ne.jp redtrendnews.blog.so-net.ne.jp/
29 207554 ksaclnlskjfslsdkj.cocolog-nifty.com ksaclnlskjfslsdkj.cocolog-nifty.com/
... ... ... ...
70 207636 ykore.hatenablog.com ykore.hatenablog.com/
71 207680 kimagure-koi.xyz kimagure-koi.xyz/
72 207680 internasional.kompas.com internasional.kompas.com/
73 207680 sepia.openmatrix.net sepia.openmatrix.net/
74 207680 news2021-4-14.blogspot.com news2021-4-14.blogspot.com/
75 208088 konamania.exblog.jp konamania.exblog.jp/
76 208088 aroundthirty-dokujyo.hateblo.jp aroundthirty-dokujyo.hateblo.jp/
77 208088 yutori-simple.com yutori-simple.com/
78 208088 miniyagikoyuki.cocolog-nifty.com miniyagikoyuki.cocolog-nifty.com/
79 207960 machinoongakushitsu.hida-ch.com machinoongakushitsu.hida-ch.com/
80 207960 dot.asahi.com dot.asahi.com/aera/2015110400007.html
81 208127 kitakyushubank.co.jp kitakyushubank.co.jp/
82 208130 stock.searchina.ne.jp stock.searchina.ne.jp/data/code.cgi
83 208135 pex.jp pex.jp/amazon_spend/complete?
84 207484 megusoku.com megusoku.com/
85 207487 7462.bazoooka.org 7462.bazoooka.org/
86 207487 healthcare.itmedia.co.jp healthcare.itmedia.co.jp/
87 207506 geinou-today.click geinou-today.click/
88 208088 shouchiku-school.seesaa.net shouchiku-school.seesaa.net/
89 208088 narutaki.hatenablog.com narutaki.hatenablog.com/
90 208089 akai-hideki.hatenablog.com akai-hideki.hatenablog.com/
91 207841 asi17.blog.jp asi17.blog.jp/
92 207841 67404560.at.webry.info 67404560.at.webry.info/
93 208148 www.nurse-agent.com www.nurse-agent.com/
94 207415 midori7614.exblog.jp midori7614.exblog.jp/
95 208035 www.webcg.net www.webcg.net/articles/hiding_WEBCG-impression...
96 208035 response.jp response.jp/special/recent/2800/%E3%82%B9%E3%8...
97 208035 response.jp response.jp/article/2014/8/7/229522.html?
98 208035 response.jp response.jp/article/img/2014/8/21/230421/73813...
99 208035 response.jp response.jp/article/img/2014/8/21/230391/73899...

100 rows × 3 columns

In [515]:
#pd.read_csv('/home/dacdata/2021/appendix/PRISM3_mapping_master.gz')
Out[515]:
300047 300034 2 ビジネススクール
0 300111 300103 3 長崎県
1 300175 300171 2 PCソフトウェア
2 300239 300238 3 Android対応アプリケーション
3 300303 300287 2 芸能(国内)
4 300367 300358 3 アニメグッズ
5 300431 300430 3 フィギュア
6 300495 300488 3 バーベキュー
7 300559 300558 3 ツアー旅行
8 300623 300616 3 千葉県
9 300687 300666 2 コーディネート・スタイル
10 300751 300730 2 麺類
11 300815 300812 3 生物学
12 300879 300866 3 茨城県
13 300943 300919 3 三重県
14 301007 300967 3 福岡県
15 301071 301063 3 茨城県
16 301135 301112 3 岐阜県
17 301199 301160 3 高知県
18 301263 301208 2 パーツ&アクセサリ
19 300053 300034 2 世界経済・国際経済
20 300117 300114 3 エンジニア・技術系
21 300181 300175 3 アップローダー
22 300245 300240 3 格安SIM
23 300309 300303 3 俳優・女優
24 300373 300369 3 アドベンチャーゲーム
25 300437 300435 3 競輪
26 300501 300498 3 演劇
27 300565 300523 2 ホテル・宿泊施設
28 300629 300628 3 静岡県
29 300693 300689 2 化粧品
... ... ... ... ...
1259 300679 300671 3 メンズファッション
1260 300743 300739 3 サワー・酎ハイ
1261 300807 300803 3 経営学
1262 300871 300866 3 関西圏
1263 300935 300919 3 長野県
1264 300999 300967 3 島根県
1265 301063 300852 2 中古戸建
1266 301127 301112 3 山梨県
1267 301191 301160 3 鳥取県
1268 301255 301221 3 オートバイ全般
1269 300027 300025 3 電力
1270 300091 300085 3 滋賀県
1271 300155 300153 3 住宅ローン
1272 300219 300217 3 スポーツSNS
1273 300283 300278 2 歴史
1274 300347 300342 3 バラエティ番組
1275 300411 300409 3 工芸・ガラスアート
1276 300475 300445 2 サッカー
1277 300539 300536 3 中国旅行
1278 300603 300598 3 イタリア
1279 300667 300666 2 ファッション誌
1280 300731 300730 2 菓子・デザート
1281 300795 300791 3 その他言語
1282 300859 300852 2 売却
1283 300923 300919 3 秋田県
1284 300987 300967 3 福井県
1285 301051 301015 3 香川県
1286 301115 301112 3 岩手県
1287 301179 301160 3 石川県
1288 301243 301221 3 ハーレーダビッドソン

1289 rows × 4 columns

In [ ]:
 
In [282]:
def count_missing_data(file_path, debug=False):
    print("Start: count_missing_data", file=sys.stderr)

    if debug:
        tracemalloc.start()

    #df = makedat(file_path)
    total_missing = 0
    with gzip.open(file_path,'r') as file:
        count = 0
        dat = []
        for line in file:
            l = line.decode('utf-8').split(',')
            l[12] = ','.join(l[12:]) #前の13列以降を結合する
        total_missing  += (pd.DataFrame(l).isnull() | pd.DataFrame(l)== "").sum()

    return total_missing
In [283]:
count_missing_data(daclog_files[0])
Start: count_missing_data
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-283-dea8ad40e021> in <module>
----> 1 count_missing_data(daclog_files[0])

<ipython-input-282-4820ecb31bb3> in count_missing_data(file_path, debug)
     10         count = 0
     11         dat = []
---> 12         for line in file:
     13             l = line.decode('utf-8').split(',')
     14             l[12] = ','.join(l[12:]) #前の13列以降を結合する

~/anaconda3/lib/python3.7/gzip.py in readline(self, size)
    372     def readline(self, size=-1):
    373         self._check_not_closed()
--> 374         return self._buffer.readline(size)
    375 
    376 

~/anaconda3/lib/python3.7/_compression.py in readinto(self, b)
     66     def readinto(self, b):
     67         with memoryview(b) as view, view.cast("B") as byte_view:
---> 68             data = self.read(len(byte_view))
     69             byte_view[:len(data)] = data
     70         return len(data)

~/anaconda3/lib/python3.7/gzip.py in read(self, size)
    469             buf = self._fp.read(io.DEFAULT_BUFFER_SIZE)
    470 
--> 471             uncompress = self._decompressor.decompress(buf, size)
    472             if self._decompressor.unconsumed_tail != b"":
    473                 self._fp.prepend(self._decompressor.unconsumed_tail)

KeyboardInterrupt: